Generative AI tools like ChatGPT are extremely intuitive due to their conversational nature. It raises the question, why does there need to be a methodology or framework?

To answer this, it is important to have the perspective that computing is just a collection of calls and responses, and language is the medium that allows call and response to communicate.

Assume you go to a customer service counter and place an item on the desk. You could say the following things (the call):

• Say Nothing

• I want to buy this item

• I want to return this item

• I want to buy this item with my American Express, packaged in cardboard box, and delivered to 100 Little St. Big City CO via UPS Next-Day shipping at 3pm EST.

• Quiero comprar este artículo con mi American Express, empaquetado en una caja de cartón y entregado en 100 Little St. Big City CO a través de UPS Next-Day shipping a las 3:00 p. m. EST.

Based on what you say and what is understood by the person standing behind the counter, you could get a series of responses or a single response. The same is true with a large language model and by understanding what a large language models knows and how it understands requests will allow you to more effectively communicate with it.

The purpose of GDF is not to slow down teams or individuals with yet another process, but to simply provide patterns and principles that can be implemented to whatever extent is efficient and secure.

This documentation introduces a human-centric development framework called the Generative Development Framework (GDF). The GDF consists of nineteen knowledge areas encompassing cognitive strategies, techniques, and patterns to facilitate more efficient use of generative technologies, with an emphasis on security, risk management, and ethical considerations.

The GDF is designed to be broadly applicable across different use cases and generative models. While examples are provided for specific contexts, the framework remains adaptable to various domains. Users are encouraged to customize the prompts and processes to meet their specific needs.

GDF is intended to be used selectively, with practitioners adopting only the knowledge areas and processes that are productive for their teams. Successful implementation of GDF should involve continuous evaluation and improvement, adjusting the framework to fit organizational needs and to reflect ongoing learning.

For new users and developers unfamiliar with generative technologies, it is highly recommended to read through the entire documentation to gain a comprehensive understanding of the concepts and associated terminology. For those with experience, the framework may be referenced on an as-needed basis.

Origins of GDF

The Generative Development Framework (GDF) has emerged from years of experience in various domains. With the rapid evolution of generative AI in recent months, these technologies have reshaped work processes, prompting new methodologies to effectively integrate AI-driven techniques. GDF represents a distillation of these learnings, documenting both effective and ineffective practices.

Leveraging generative AI has fundamentally altered how individuals engage with their work. It has provided opportunities to prioritize quality and innovation, reducing the need to manually perform repetitive tasks or spend excessive time troubleshooting or researching implementation details. Furthermore, the conversational nature of generative AI tools has facilitated a collaborative dynamic, enabling more engaging workflows and sparking more frequent “aha” moments through interactive dialogue rather than passive consumption of static content.

While the potential of these generative tools is exciting, successful implementation requires specific considerations to effectively manage and mitigate new risks introduced by generative technologies. By the end of this documentation, users should have gained the foundational knowledge needed to implement generative tools, manage associated risks, and leverage these technologies to enhance workflows.

Scope of GDF

The GDF aims to provide a comprehensive framework for understanding the integration of generative technologies across various organizational teams, with particular attention to managing onboarding processes for individuals, promoting effective use of generative tools, and addressing the associated risks. The framework is not limited to a specific field or development role but instead promotes a holistic view of generative technology adoption, emphasizing ethical considerations, secure implementation, and strategic onboarding processes that address the diverse needs of an enterprise.

Before discussing how using GDF can improve processes, it is important to have a good understanding of what generative AI and large language models (LLMs) are.

What is Generative AI?

Generative AI is a type of artificial intelligence that involves the use of machine learning algorithms to generate new and original content, such as images, videos, text, or music. Unlike traditional machine learning algorithms, which are typically used to classify or predict data based on existing patterns, generative AI is used to create new patterns or data.

Generative AI typically involves the use of deep learning models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). These models are trained on large datasets of existing content and are then able to generate new content that is similar in style or structure to the original data.

What are Large Language Models?

Large language models are artificial intelligence models that are designed to process and generate human language. They use deep learning algorithms and neural networks to analyze and understand language and are trained on large datasets of text to learn patterns and structures in human language.

Large language models can be used for a wide range of natural language processing tasks, including text classification, sentiment analysis, machine translation, question answering, and conversational systems. They can also generate new text that is similar in style or structure to the input text, making them useful for applications such as content creation, text summarization, and language generation.

One of the key advantages of large language models is their ability to learn from vast amounts of data, allowing them to understand and generate human language at a scale that was previously impossible. However, the training and development of large language models also require significant computing resources and energy, which can be a barrier to entry for many users and organizations.

How Machine Learning Works

Consider the words "kind of works like this." A language model would take this sentence along with billions of other text materials and break it down into tokens:

• KIND

• KIND OF

• KIND OF WORKS

• WORKS LIKE

• WORKS LIKE THIS

The model processes these tokens and assigns weights based on context. For instance, if the training data includes explanations or high-level descriptions, the model may determine that the phrase "kind of works like this" has a high probability of being accurate in similar contexts. By processing extensive amounts of text, generative models learn to produce sophisticated, human-like responses that can be useful in a variety of scenarios.

Below is a flow that visualizes a typical machine learning process.

1. Data Collection: The first step in any machine learning pipeline is gathering a large and diverse dataset. For text-based models, this involves collecting text data from a wide range of sources, ensuring the dataset is both comprehensive and representative. This diversity allows the model to generalize effectively across different types of inputs and tasks but also introduces challenges, such as managing varying levels of quality and addressing potential biases in the data.

2. Data Cleaning: After collection, the data undergoes cleaning to remove irrelevant, noisy, or incorrect information. This step is essential to ensure the model learns from valuable and meaningful patterns rather than being misled by errors, redundant data, or inappropriate content. The goal is to reduce the dataset’s complexity, making it easier for the model to focus on significant and consistent signals.

3. Data Preprocessing: Preprocessing prepares the raw data for the model by transforming it into a suitable format. This typically involves tokenization (breaking down text into smaller units), numerical encoding (converting tokens into vectors or embeddings), and splitting the data into training, validation, and test sets. These steps ensure that the model can process the data effectively and that performance evaluations are unbiased, allowing for a robust assessment of the model’s capabilities.

4. Training: During training, the model learns patterns and structures in the data using a deep learning architecture. This process involves optimizing a mathematical objective (e.g., minimizing a loss function) through iterative updates. The model learns to make increasingly accurate predictions by adjusting its parameters based on the patterns it detects in the data. This phase is computationally intensive, often requiring powerful hardware and efficient algorithms to handle the vast amounts of data.

5. Model Tuning: Once the model has been trained, fine-tuning adapts it to specific tasks, such as text classification or language translation. Fine-tuning helps the model perform well on particular problems by using task-specific datasets and objectives. This step increases the model's utility by making it versatile across a variety of use cases, building on the general patterns learned during the initial training phase.

6. Deployment: After achieving satisfactory performance, the model is deployed into real-world applications. This could involve integrating the model into cloud-based services, mobile applications, or APIs. During deployment, practical considerations such as latency, scalability, and reliability come into play to ensure the model can serve predictions efficiently under varying loads and user demands.

Challenges in Model Development and Application:

In developing machine learning models, especially large-scale models trained on diverse text corpora, variability in data quality is a common challenge. Text data collected from various sources may contain inaccuracies, inconsistencies, or less-than-optimal patterns. This presents difficulties when applying models in real-world scenarios, where reliable outputs are critical.

A key consideration is that not all data sources are equally trustworthy or precise, which means the model might learn both useful and suboptimal patterns. This reflects the balance between leveraging the broad scope of available data and mitigating the risks associated with potential inaccuracies. Despite these challenges, the sheer volume and diversity of data generally allow models to learn robust and useful patterns that perform well across a wide range of tasks.

Even when models generate outputs that are valid or accurate, these outputs may not always be optimal. For instance, in domains such as content generation, problem-solving, or automated decision-making, the outputs may work in a technical sense but could be improved in terms of efficiency, correctness, or alignment with specific standards. Therefore, while machine learning models can provide valuable assistance in many domains, human oversight remains crucial. Reviewing, validating, and improving outputs ensures that the final results are both reliable and suited to the specific context in which they are applied.

In practice, this means that machine learning models are powerful tools, but they should be used with care. The integration of these models into larger workflows requires attention to detail, including thorough evaluation and quality assurance, to ensure they meet the desired performance and safety standards. This combination of advanced machine learning techniques and diligent oversight allows models to be applied effectively in real-world settings, addressing both broad and specialized challenges.

Like any tool or application, knowing how to efficiently use it can drastically affect productivity. Take an image creation application for example, just about anybody can bring it up, start clicking on brushes, and draw a picture. But a vision, expertise in layers, transformations, cutting, selection, and doing it all efficiently is what allows artists to create works of art productively.

When we think about AI generated content, we want to consider the GDF knowledge domains:

Knowledge Areas

• Ethics

• Limitations

• Context Management

• Security Management

• Risk Management

• Adoption

• Prompting

• Subject Knowledge Areas

  • Ideation

  • Specification

  • Generation

  • Transformation

  • Replacement

  • Integration

  • Separation

  • Consolidation

  • Templating

  • Visualization

  • Verification

  • Implementation

  • Optimization

• Generative Pipelines

For example:

Using AI for code ideation

Below are detailed prompt examples examples with references to a fictional application I will be be building throughout the documentation, which will be a bicycle rental application.

If you are unfamiliar with programming, many of the prompts below may be difficult to understand. By the end of this course you should have a better understanding of these terms and have the right troubleshooting knowledge to resolve any issues you may run into.

Example Prompts:

• Ideation:

  • What would I need to build a bicycle rental application?

  • What languages are used to build a mobile application?

  • What languages are used to build a web application?

  • Are there any languages that would allow me to build on both web and mobile at the same time?

  • What is react?

  • What is swift?

  • What is kotlin?

  • What are the main parts of an app layout?

  • What libraries are used to build an app interface in react?

  • What software do I need to create a react app on my computer?

  • How do I run a react app on my computer?

  • How to install react in visual studio code?

  • How to install node.js?

• Generation

  • Create a code sample in react that renders a navigation bar relevant to things a bicycle renting customer might want to do?

  • Create a code sample in react that renders a home page layout to rent a bicycle.

• Specification

  • Use Chakra UI as the UI library in the code above instead of material-ui.

  • Use NextJS router for routing

• Transformation

  • Convert the navigation bar into a drawer navigation

  • Convert the code sample to NextJS

  • Convert the code to angular js.

• Replacement

  • Replace the navigation items in the NavBar component with the following items

  • Replace the body with some example components for a bicycle renting application

• Integration

  • Create a code sample that would get a list of bicycles and their locations

  • Import the above code sample into Home component we created earlier to load in the bicycle data.

• Separation

  • Create new react components using chakra ui for the components you created in the body earlier

  • Separate the navigation items into a separate file with a callout to get the data.

• Consolidation

  • Merge the NavBar component and Logo component into a single NavBar component

  • Consolidate the processing of the bicycle data with the function to transform the date into hh:mm MM/DD.

• Templating

  • create a layout in next.js for header, body, and footer. use this layout to create a contact page.

• Visualization

  • Change the styling of the nav bar to be more like an apple navbar.

  • Make the navigation dynamic and mobile friendly.

• Implementation

  • How do I upload a react app on the web?

  • How do I point a domain name to a react app?

• Optimization

  • Remove any unnecessary comments from the code.

  • Optimize the parsing of the data to be less redundant.

Programming languages like JavaScript are used to create web applications, which are software programs that are accessed and run over the internet through a web browser. When a user accesses a web application, the code written in JavaScript is executed by the web browser, which translates the code into machine-readable instructions that the computer can understand and execute.

Web applications are typically composed of three main parts: the client-side code, the server-side code, and the database. The client-side code is written in JavaScript and runs on the user's web browser, while the server-side code is written in a server-side programming language like Node.js and runs on a web server. The database is used to store and retrieve data used by the web application.

When a user interacts with a web application, the client-side code sends requests to the server-side code, which processes the requests and sends responses back to the client-side code. This process is known as client-server communication and is a fundamental part of how web applications work.

In terms of compilation, web applications like those created using JavaScript are typically not compiled in the traditional sense. Instead, the client-side code is interpreted by the web browser at runtime, meaning that the code is translated into machine-readable instructions on the fly, as it is executed. This is in contrast to compiled programming languages like C++ or Java, where the code is compiled into machine code before it is executed.

Overall, programming languages like JavaScript are essential for creating web applications, which are widely used by people all over the world for a variety of purposes. The process of creating and running web applications involves a complex interaction between client-side code, server-side code, and a database, all of which work together to provide users with a rich and interactive web experience.

describe programming languages in the context of javascript and web apps

Computer science is a vast field that encompasses various disciplines, from the theoretical foundations of algorithms and data structures to the practical aspects of software development and computer systems. Understanding the key concepts in computer science is essential for anyone interested in programming, software development, or pursuing a career in the field. In this article, we will delve into space-time complexity and other core computer science concepts.

Space-Time Complexity

Space-time complexity refers to the combined measurement of the resources, i.e., time and memory, that an algorithm uses to solve a problem. Analyzing an algorithm's space-time complexity helps determine its efficiency and scalability. Two key aspects of space-time complexity are:

Time Complexity

Time complexity is the amount of time an algorithm takes to execute as a function of the input size. Commonly used notations to describe time complexity are Big O (O), Omega (Ω), and Theta (Θ).

Space Complexity

Space complexity is the amount of memory an algorithm uses as a function of the input size. Like time complexity, space complexity is also described using Big O, Omega, and Theta notations.

Algorithms and Data Structures

Algorithms are step-by-step procedures for solving problems, while data structures are ways of organizing and storing data. Understanding and choosing the right algorithms and data structures for a particular problem is crucial to achieving optimal performance in software applications.

Programming Paradigms

There are several programming paradigms, each with its own set of principles and techniques. Some common paradigms include:

Imperative Programming

Focuses on describing the steps to solve a problem using statements that change a program's state.

Object-Oriented Programming (OOP)

Organizes code using objects and classes, emphasizing the use of encapsulation, inheritance, and polymorphism.

Functional Programming

Treats computation as the evaluation of mathematical functions, avoiding mutable state and side effects.

Operating Systems

An operating system (OS) is the software that manages computer hardware and software resources. It provides an interface between the user and the hardware, enabling the execution of various applications. Key OS concepts include processes, threads, memory management, and file systems.

Computer Networks

Computer networks enable communication and data exchange between computers and other devices. Understanding networking concepts, such as the OSI model, IP addressing, routing, and transport protocols, is essential for developing network applications and managing network infrastructure.

Databases

Databases store and manage data in a structured and organized manner. Familiarizing oneself with database management systems (DBMS), data models (e.g., relational, NoSQL), and query languages (e.g., SQL) is vital for developing data-driven applications.

Software Engineering

Software engineering is the discipline of designing, developing, and maintaining software systems. It encompasses methodologies (e.g., Agile, Waterfall), software architecture, testing, and version control, among other topics.

Computer science is a multifaceted field with numerous concepts and principles that guide the development and management of computer systems and software applications. Understanding space-time complexity, algorithms, data structures, programming paradigms, operating systems, computer networks, databases, and software engineering is vital for excelling in the world of computer science.

Throughout this documentation, I will be making references to a fictional bicycle rental application that I will be developing.

The problem statement for the business is the low utilization of bicycles in America. A study from PeopleForBIkes found that in 2019 that only 11% rode a bicycle at least once a week where 39% of the adult population owns a bicycle. To solve this problem, a platform will allow bike owners to list their bicycles on a rental marketplace and customers who want to ride a bicycle can rent those bicycles for a given time interval. We will talk about ideation in detail later in the documentation. It is important to note that this fictional application does not stand up to the scrutiny of defining problem statement for an actual business. In this documentation we do not perform the research expected of an actual application. It is only being used as it is an application that many people can relate to.

As you go through the docs, I recommend you follow along and build an application as well. It can be the bicycle rental application or if you are interested in your own application, feel free to build that instead.

In the sub sections, you will find step by step instructions for installing the following tools (all of which are free and open source):

In this documentation, I will be building application using javascript and Node.js. If you are also building a web application, it may be easier to do the same as you follow along with this course. You can also use the concept of transformation to convert it into another language later if you choose.

how many americans ride their bicyles

JavaScript is a versatile programming language used for both client-side and server-side applications. React is a popular JavaScript library for building user interfaces, and Next.js is a powerful framework built on top of React that simplifies the development of scalable and performant web applications.

In this tutorial, we'll introduce some important JavaScript concepts and demonstrate how to use them with React and Next.js by building a simple bicycle rental application. We'll cover the following topics:

1. Running JavaScript code using online tools

2. Variables, Constants, and Data Types

3. Functions

4. Conditional Statements

5. Loops

6. Arrays

7. Objects

8. Asynchronous Programming

9. React Components

10. Next.js Pages

Prerequisites

To follow along, you'll need to have Node.js and npm (Node package manager) installed on your computer. If you don't have them, refer to the.

0. Running JavaScript code using online tools

Before we dive into the code, you may want to try out some JavaScript code snippets without setting up a complete project. Online tools like JSFiddle () or CodeSandbox () allow you to write, run, and share JavaScript code directly in your browser.

To get started, visit one of these websites, create a new fiddle or sandbox, and start writing your JavaScript code in the designated area.

Getting Started

First, let's create a new Next.js application using the terminal:

1. Variables, Constants, and Data Types

JavaScript has three ways to declare variables: var, let, and const. We'll be using let and const in this tutorial.

JavaScript supports several data types, such as strings, numbers, booleans, and objects.

2. Functions

Functions are blocks of code that can be defined and called by name. Functions can take parameters and return values.

3. Conditional Statements

Conditional statements execute code based on certain conditions. The most common conditional statement is the if statement.

4. Loops

Loops are used to execute a block of code repeatedly. The most common loop in JavaScript is the for loop.

5. Arrays

Arrays are used to store multiple values in a single variable.

6. Objects

Objects are a collection of properties and methods.

7. Asynchronous Programming

JavaScript supports asynchronous programming using callbacks, Promises, and the async/await syntax.

8. React Components

React components are the building blocks of the user interface. Components can have state and props, and they return JSX to render the UI.

Create a new file BicycleList.js in the components folder:

9. Next.js Pages

Next.js automatically creates a route for each page in the pages folder. To display the list of bicycles, create a new file index.js in the pages folder:

Before we get into the documentation, be sure to have a ChatGPT account set up.

Go to and create an account.

Once you have created your account and verified your email you will be in the ChatGPT console.

At the bottom of the screen you will see a text input to insert text and on the top left you will have a new chat button. To generate a response type in the input text and click send. GPT will generate a response back to you.

GPT keeps a of the questions and responses in any given chat. As an example, let’s ask GPT to

Then ask it to

If you're seeing responses, then you have successfully signed up and ChatGPT is working as expected. The prompts and concept above is extremely relevant to us as developers when we are constantly referring to previously generated items asking for help on exceptions or conversions from one language, library, or component to another. The concept of memory in a large language model is called "prompt context" and is one of 5 concepts we will cover in the documentation.

The prospect of AI creating and delivering a production grade application from a list of features or is an alluring prospect, but is currently not possible. As AI evolves and the scope of it's capabilities extend past what it is today, human direction and intervention will inevitably carry more value. Organic decisions made outside of possible outcomes in a model will be the differentiation between new and old ideas. To a greater degree, expertise will be defined by those who understand the innerworkings of what is generated, how it's generated, and most importantly what it means.

GDF does not provide the capability to instantly create an application, but instead provides processes that accelerate and improve the development process through patterns and discussion of different generative AI perspectives. However, these knowledge areas can be applied and systematically iterated upon to provide this capability to an extent. This concept is called . To be clear, developers should fully expect to think critically, think creatively, understand programming concepts, read documentation, and troubleshoot issues.

Installing Visual Studio Code on Windows

1. Open your web browser and go to the Visual Studio Code website at

2. Click on the "Download for Windows" button to download the installation file.

3. Once the download is complete, double-click on the installation file to start the setup process.

4. Follow the on-screen instructions to complete the installation process. This will include accepting the license agreement and choosing the installation location.

5. Once the installation is complete, click on the "Finish" button to close the setup wizard.

6. Visual Studio Code will now be installed on your computer and you can start using it.

Installing Visual Studio Code on Mac

1. Open your web browser and go to the Visual Studio Code website at

2. Click on the "Download for Mac" button to download the installation file.

3. Once the download is complete, double-click on the installation file to start the setup process.

4. Follow the on-screen instructions to complete the installation process. This will include accepting the license agreement and choosing the installation location.

5. Once the installation is complete, drag Visual Studio Code to the Applications folder.

6. You can now start Visual Studio Code by double-clicking on its icon in the Applications folder.

7. Visual Studio Code will now be installed on your Mac and you can start using it.

Setting up a Next.js project with Chakra UI involves several steps, including installing the necessary dependencies, configuring your project, and adding basic components.

If you followed the instructions from the section, you can skip to the

Follow these step-by-step instructions to get started:

Install Node.js and npm

Ensure that you have Node.js (version 12 or higher) and npm (version 6 or higher) installed on your system. You can check the installed versions by running the following commands in your terminal or command prompt:

Create a new Next.js project

Run the following command to create a new Next.js application using the default starter template:

Replace your-app-name with your desired application name.

Change to the project directory

Navigate to the newly created project directory by running:

Install Chakra UI dependencies

In the project directory, run the following command to install Chakra UI and its peer dependencies:

Install Chakra UI icons (optional)

If you want to use Chakra UI's icon library, run the following command to install:

Set up a custom _app.js file

In the "pages" directory of your project, create or edit the _app.js file to include the ChakraProvider. Replace the existing content with the following code:

Start using Chakra UI components

Now, you can start using Chakra UI components in your Next.js project. As an example, open the "pages/index.js" file and replace its content with the following code:

Run the development server

In the terminal or command prompt, run the following command to start the development server:

The use of AI in code generation is revolutionizing the software development landscape. However, building trust with colleagues and the wider development community is essential for the successful adoption of AI-generated code. In this article, we will discuss the importance of transparency in AI-generated code and how to maintain trust while benefiting from AI's efficiency gains.

The importance of transparency in AI-generated code

• Builds trust among colleagues and the development community by openly acknowledging the use of AI in code generation.

• Ensures that others are aware of potential limitations and can review the AI-generated code with the understanding that it may require refinement or validation.

• Promotes collaboration and constructive feedback by fostering open communication about the use of AI and its role in the development process.

Recognizing AI as a tool

• Emphasize that AI is a tool, not a replacement for human expertise and creativity.

• Understand that interpreting AI-generated code and making decisions based on that understanding still requires human intervention.

• Value the role of human expertise in refining and validating AI-generated code, ensuring the final product meets quality and performance standards.

Embracing AI to enhance personal expertise

• Don't let insecurities about your knowledge prevent you from leveraging AI in your development process.

• Use the efficiency gains from AI to invest more time in learning core concepts and mastering syntax, enhancing your overall expertise.

• Share your learnings and experiences with AI-generated code with your colleagues, fostering a culture of learning and continuous improvement.

Conclusion

Transparency in AI-generated code is key to building trust within your team and the broader development community. Recognizing AI as a valuable tool, while maintaining the importance of human expertise, allows developers to embrace AI in their toolbox confidently. By being transparent about the use of AI-generated code and using the time saved to enhance personal expertise, developers can contribute to a culture of trust, collaboration, and continuous learning in the software development landscape.

Installing Node.js and npm on Windows

1. Open your web browser and go to the Node.js website at https://nodejs.org/

2. Click on the "Download" button to download the latest version of Node.js for Windows.

3. Once the download is complete, double-click on the installation file to start the setup process.

4. Follow the on-screen instructions to complete the installation process. This will include accepting the license agreement and choosing the installation location.

5. Once the installation is complete, click on the "Finish" button to close the setup wizard.

6. To verify that Node.js has been installed successfully, open the Command Prompt and type "node -v". This should display the version of Node.js that you have installed.

7. To verify that npm has been installed successfully, open the Command Prompt and type "npm -v". This should display the version of npm that you have installed.

Installing Node.js and npm on Mac

1. Open your terminal and run the following command to install Homebrew:

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"

1. Once Homebrew is installed, run the following command to install Node.js:

brew install node

1. To verify that Node.js has been installed successfully, run the following command in your terminal:

node -v

1. To verify that npm has been installed successfully, run the following command in your terminal:

npm -v

Both Node.js and npm should now be installed and ready to use on your Mac.

create a step by step instruction on installing node.js and npm on windows and mac

Like humans, large language models do not handle ambiguity well. The responses to ambigous unguided prompts are often high level and summarized. This is where the huamn driven factor of AI driven development is extremely valuable.

GPT cannot generate fully functioning or integrated applications in a single request. For example, you cannot simply tell GPT to create a bicycle rental application and expect a fully functioning application as an output.

Type it in as a prompt and see what happens: “Create a bicycling rental web application where users can rent a bike”.

GPT provides the following response:

1. Define the scope of the application: What kind of bikes will be offered for rent? What are the rental rates? What are the rental durations? Will the application offer any discounts or promotions? These are just a few of the questions that need to be answered when defining the scope of the application.

2. Develop a user interface (UI) for the application: The UI should be designed in a way that makes it easy for users to browse bikes, select rental options, and make payments.

3. Implement a bike inventory system: The application needs to keep track of the number of bikes available for rent, their location, and their rental status.

4. Implement a payment system: The application should offer a secure payment gateway that allows users to pay for their bike rentals using credit cards, PayPal, or other payment methods.

5. Implement a reservation system: Users should be able to reserve bikes in advance and receive confirmation of their reservation via email or SMS.

6. Implement a user management system: The application should allow users to create and manage their profiles, view their rental history, and update their payment information.

7. Implement a feedback system: The application should allow users to provide feedback on their rental experience, which can be used to improve the service in the future.

8. Launch and market the application: Once the application is ready, it can be launched and marketed through social media, email campaigns, and other channels to attract users.

9. Monitor and optimize the application: It's important to monitor the application's performance regularly and make improvements as needed to ensure that users have a positive experience.

These are just a few of the steps involved in creating a bicycling rental web application. Depending on the specific requirements of the application, additional steps may be necessary. It's also important to work with a team of experienced developers who can help bring your vision to life.

While the above response is useful and gives us features and systems to consider, it does not output any code.

Most large language models are typically not trained to lie. They simply evaluate the prompt against its model (the data it was trained on), and return a response. These models are updated at a point and time, meaning they may have been built on data that is days, months, or years old. And while responses can sound very confident, AI can be incorrect through its misunderstanding of context, application or simply be out of date.

Time Accuracy

While this applies to all questions, this is especially true in software development due to the fast pace of change within languages, libraries, and platforms.

Scope

Another limitation to LLMs is the scope of it's knowledge. Let's say there is an updated charting library being using in existing development framework like React. A particular exception and it's solution may not be something that the model has been trained on and may not be able to answer that question. Or if a certain method in a framework was never documented publicly, the LLM may respond that the method does not exist when it really does.

Summary

No matter how rapidly AI is refined, there will always be the possibility for AI to create code that results in exceptions that it cannot solve. I have encountered a few situations where GPT is continually generating clean code that compiles without issue, and suddenly gets caught on something that it cannot resolve. Have patience and run through a structured troubleshooting process. A process for troubleshooting is provided at the end of this course, as a resource for anytime you run into an issue while developing. To be successful, have the expectation that AI is a tool to build applications and cannot be used an “app generator” at this point and time.

How to check last updated data?

Checking when data on model on was last updated will like vary per model.

For ChatGPT simply ask it as a prompt.

Prompt

when was this model data last updated

Response

This model's data was last updated in September 2021.

"Undue harm" refers to harm or injury that is excessive or unnecessary in relation to the benefits of a particular action. The legal definition of undue harm may vary depending on the jurisdiction and the context in which it is used.

In general, the concept of undue harm is often used in legal and ethical frameworks to assess the risks and benefits of a particular action or decision. For example, in the context of medical treatments or procedures, undue harm might refer to harm that is disproportionate to the benefits of the treatment or that could have been avoided with alternative treatments or procedures.

In the context of generative AI, the use cases are vast and will undoubtedly be used in ways that cause "undue harm".

"Undue harm" was used specifically knowing that AI may be used to defend ideas that are good. We will not define what is good vs. bad in this framework and leave that to the discretion of it's users.

Generative References

what is the legal definition of undue harm

As your prompts get more specific and contextual, you will find that writing out the same specifiers over and over again is tiring. To alleviate this, you can provide context of shorthands or variables within your conversation that let the model know that a specific prompt means something to you, welcome prompt stores.

For example, in a bicycle rental application that uses React, Next.js, Chakra UI, Yup, and Axios libraries. You would not want to have to write create a contact form using react, next.js, chakra ui, yup, and axios and then for the sign up page rewrite create a sign up page using react, next.js, chakra ui, yup, and axios.

Example

Create a prompt store to store language and libraries

Prompt

chatgpt-3.5: 
when I write bicycle-app-config remember that I mean "next.js, chakra ui, yup, and axios"

Response

Sure, I'll keep that in mind! When you write bicycle-app-config, I'll assume that you mean a configuration that includes Next.js, Chakra UI, Yup, and Axios.

Discussion

Perfect. Now that we have a prompt store that retains the context of the languages and libraries we use, let's see it in practice.

Prompt

create a contact form, bicycle-app-config

Response

Sure, here's an example of how you can create a contact form in a Next.js app with Chakra UI, Yup, and Axios, using the bicycle-app-config:

import React from "react";
import { Box, Button, FormControl, FormErrorMessage, FormLabel, Input, Textarea } from "@chakra-ui/react";
import { useFormik } from "formik";
import * as Yup from "yup";
import axios from "axios";

const contactSchema = Yup.object().shape({
  name: Yup.string()
    .required("Name is required")
    .matches(/^[a-zA-Z\s]*$/, "Invalid name format"),
  email: Yup.string().email("Invalid email format").required("Email is required"),
  message: Yup.string().required("Message is required"),
});

const ContactForm = () => {
  const formik = useFormik({
    initialValues: {
      name: "",
      email: "",
      message: "",
    },
    validationSchema: contactSchema,
    onSubmit: async (values, { setSubmitting }) => {
      try {
        const response = await axios.post("/api/contact", values);
        console.log(response.data);
      } catch (error) {
        console.error(error);
      } finally {
        setSubmitting(false);
      }
    },
  });

  return (
    <Box>
      <FormControl id="name" isInvalid={formik.touched.name && formik.errors.name}>
        <FormLabel>Name</FormLabel>
        <Input type="text" {...formik.getFieldProps("name")} />
        <FormErrorMessage>{formik.errors.name}</FormErrorMessage>
      </FormControl>
      <FormControl id="email" isInvalid={formik.touched.email && formik.errors.email}>
        <FormLabel>Email</FormLabel>
        <Input type="email" {...formik.getFieldProps("email")} />
        <FormErrorMessage>{formik.errors.email}</FormErrorMessage>
      </FormControl>
      <FormControl id="message" isInvalid={formik.touched.message && formik.errors.message}>
        <FormLabel>Message</FormLabel>
        <Textarea {...formik.getFieldProps("message")} />
        <FormErrorMessage>{formik.errors.message}</FormErrorMessage>
      </FormControl>
      <Button mt="4" colorScheme="teal" isLoading={formik.isSubmitting} onClick={formik.handleSubmit}>
        Submit
      </Button>
    </Box>
  );
};

export default ContactForm;

In this example, we're using Chakra UI's FormControl, FormErrorMessage, FormLabel, Input, and Textarea components to create a form that includes the fields for name, email, and message. We're also using the useFormik hook from formik to manage the form state and handle form submission.

We're passing the contact form schema to the validationSchema prop of the useFormik hook to validate the form data against the schema. The isInvalid prop of the FormControl component is set to true if the field has been touched and there is a validation error.

Finally, we're using the getFieldProps function from formik to bind each form input to the corresponding formik field, and using the handleSubmit function from formik to handle form submission. We're also using Axios to make an asynchronous POST request to the /api/contact endpoint when the form is submitted.

Note that in order for this example to work, you'll need to create an API route in your Next.js app at /pages/api/contact.js that handles the form submission and sends an email or performs other actions based on the submitted data.

Discussion

Using prompt stores can greatly expedite your development delivery while bringing unity and standardization to your response output. The concept of prompt stores can not only be applied languages, but can also be applied to libraries, UI layouts, business rules, formats, security schemas, user scenarios, or just about any repeatable concept you can think of.

Prompt operators allow conditional statements to be applied to commands, target, and or specifiers.

Prompt Operator Example

Prompt

chatgpt-3.5: 
create a navigation bar or drawer for a bicycle rental web app in react chakra ui

Response

Here's an example of how you can create a navigation bar using React Chakra UI:

import { Box, Flex, Link, Text } from "@chakra-ui/core";

const Navbar = () => (
  <Flex
    as="nav"
    align="center"
    justify="space-between"
    wrap="wrap"
    padding="1.5rem"
    bg="teal.500"
    color="white"
  >
    <Flex align="center" mr={5}>
      <Text fontSize="xl">Bicycle Rental</Text>
    </Flex>

    <Box display={{ sm: "block", md: "none" }} onClick={toggle}>
      <svg
        fill="white"
        width="12px"
        viewBox="0 0 20 20"
        xmlns="http://www.w3.org/2000/svg"
      >
        <title>Menu</title>
        <path d="M0 3h20v2H0V3zm0 6h20v2H0V9zm0 6h20v2H0v-2z" />
      </svg>
    </Box>

    <Box
      display={{ sm: show ? "block" : "none", md: "flex" }}
      width={{ sm: "full", md: "auto" }}
      alignItems="center"
      flexGrow={1}
    >
      <Link
        mx="3"
        fontWeight="medium"
        color="white"
        fontSize="lg"
        _hover={{ color: "teal.200" }}
      >
        Home
      </Link>
      <Link
        mx="3"
        fontWeight="medium"
        color="white"
        fontSize="lg"
        _hover={{ color: "teal.200" }}
      >
        Bikes
      </Link>
      <Link
        mx="3"
        fontWeight="medium"
        color="white"
        fontSize="lg"
        _hover={{ color: "teal.200" }}
      >
        Rentals
      </Link>
    </Box>
  </Flex>
);

export default Navbar;

This creates a navigation bar with the name "Bicycle Rental", a menu icon for small screens, and links for "Home", "Bikes", and "Rentals". You can adjust the styles to match your desired design.

Operators in prompting are typically synonymous with operators in programming languages or applications that use formulas.

Examples of Operators:

• AND

• IN

• OR

• EQUALS

• LESS THAN

• GREATER THAN

• NOT IN

• CONTAINS

Prompt Context is the concept of your prompt inferring data, mostly typically used on the target of your prompt, from previous prompts and responses.

You can use prompt context to tell a model like ChatGPT for do something to the last response, or a previous message that is identified by a specifier.

Let’s walk through a complex example.

• Prompt: “create a simple for loop in javascript”

• Response: {Simple For Loop Script}

• Prompt: “update the sample above to iterate through a list of bicycle brands”

• Response: {Updated for loop script with bicycle brand names}

• Prompt: “create a simple http request to get data”

• Response: {Simple HTTP Request Script}

• Prompt: “update the http request to return the data from the for loop response above and merge the for loop into the script”

• Response: {Integrated script with HTTP request and for loop with car data}

The “above” keyword in this example instructs ChatGPT to reference data from the conversation context.

“Above” is just one contextual word. You can use words or phrases like before ,last response , or last msg to specify a target from the conversation.

Handling Large Prompt Contexts

As you delve deeper into generative AI, you may encounter situations where the amount of text you need to feed into the language model exceeds its limits. To overcome this challenge, you can create an automated process to iterate through the context and break it into smaller, manageable chunks. This article will provide examples of how to load large amounts of data using the Express framework and Node.js.

Example 1: Loading data from a file and sending it in chunks

Suppose you have a large text file containing the data you need to process using a generative AI model. You can use the following Express server to read the file and send the data in smaller chunks:

Example 2: Paginating API data

In another scenario, you may need to load large amounts of data from an API. You can paginate the API data and load it in smaller chunks using the following Express server:

These examples demonstrate how to handle large prompt contexts by breaking the data into smaller chunks and processing them individually. By implementing such solutions, you can effectively work with large datasets and overcome the limitations imposed by generative AI models.

With so much data out there, it's easy to lose track of what actually needs to be protected. To start we will focus on two key data classifications:

PII & SII

PII stands for Personally Identifiable Information and SII stands for Sensitive Identifiable Information. PII is information that can be used to identify an individual, while SII is information that requires special handling and protection due to its sensitive nature.

Examples of PII:

1. Full name

2. Social Security number

3. Date of birth

4. Home address

5. Email address

6. Phone number

7. Driver's license number

8. Passport number

9. Credit card number

10. Bank account number

Examples of SII:

1. Medical records

2. Financial information

3. Criminal records

4. Biometric data

5. Employment records

6. Education records

7. Genetic data

8. Information related to legal proceedings

9. Personal communications

10. National security information

Techniques to remove PII and SII from data

Here are a couple different techniques that can be used to programmatically remove PII and SII from data:

1. Data masking: Replacing the sensitive information with asterisks or other characters to hide it from view.

2. Data encryption: Using encryption algorithms to protect sensitive information while it is stored or transmitted.

3. Data deletion: Deleting the sensitive information from the data set altogether.

4. Data tokenization: Replacing sensitive information with randomly generated tokens that can be used as substitutes without revealing the actual data.

5. Anonymization: Removing or obfuscating all identifying information so that it cannot be linked to a specific individual.

6. Data redaction: Removing specific information from a document or record while leaving the rest intact.

7. Differential privacy: Adding random noise to data to make it difficult to identify individuals.

8. Data minimization: Collecting and retaining only the minimum amount of data necessary for a given purpose.

9. Access controls: Limiting access to sensitive data only to authorized personnel.

10. Data retention policies: Setting limits on how long sensitive data can be stored before it is permanently deleted.

Knowing the technical terms for these data protection techniques, you can now use ChatGPT to provide examples of this.

Prompt

Response

Sure, here's a simple example of how you could use JavaScript to mask customer names:

This example defines an array of customer names and a maskName function that takes a name and returns a masked version of it. The maskName function splits the name into parts (first name and last name), masks all but the first letter of the last name, and then rejoins the parts.

Finally, the map function is used to apply the maskName function to all customer names in the array, and the masked names are logged to the console.

Discussion

It returns a script with some context, but obviously your names are not hardcoded into the sample ChatGPT provided to you. Let’s send another prompt of how you would do this with a CSV file on your desktop: “update the sample above with how I would I load names and export a new CSV from a file on my desktop”. Now you have a file that allows you to parse out the names. Addressing the question of whether to build or buy data protection software is something you or your organization is going to have to decide on. The bottom line is you need to implement a process to protect your data and public tools should never be used on data that requires protection. We talked a lot about specific data elements, but these same protections should be applied to more technical prompts such as keys, secrets, proprietary patterns, and infrastructure that may open you up to risk of attacks.

It is important that if you are working within an organization that you read your security and compliance policies and spend time aligning those requirements to their corresponding protection processes. Work with your IT security and compliance teams to get the right tooling and processes in place to protect yourself while realizing the productivity gains of AI driven development.

Generative References

Integrity and ethics is a highly disputed subject where context, culture, and experiences can drive result in two completely different conclusions on whether something has integrity or is ethical.

The purpose of integrity and ethics in regards to GDF is to advocate against undue harm and for reasonable transparency.

The limitations in this section can be generally applied to all LLMs.

The following limitations may be found in various generative tools:

• Ambiguity

• Truth

  • Time Accuracy

  • Scope

• Visualizations

As ChatGPT is this most capable and accessible in the LLM space, this course focuses on ChatGPT so we will specifically discuss the limitations and weaknesses of this platform, which may or may not be the same as other platforms.

AI can be wrong about programming concepts. In the same way GPT is not completely accurate with all code generation, we should be critical of the code generated and how it is implemented. Always thoroughly review them for accuracy and cognitive complexity. This will help build competency up in various patterns and concepts.

These limitations are far from prohibitive, but are something that must be considered during the development process. Having a good understanding of these limitations provides developers focus areas and context that make the development process and application more efficient.

Prompts are the requests you send to a language model like ChatGPT that tells the system to return a response. Prompts can be a singular request or a combination of requests. The first request in a prompt is called the root request.

In this section we discuss prompting in detail to provide a good foundation in applying processes from the various GDF knowledge areas to your prompts.

The Prompting section consists of the following key concepts:

• Prompt Patterns

• Prompt Context

• Prompt Stores

• Prompt Operators

• Prompt Chaining

Prompt chaining is the use of multiple requests and operators to get more specific information, add on information, or apply different processes to a root request in a single prompt. Let’s look at one of the examples from the above section:

In the example above, the secondary request is being used to apply a consolidation process to the root request.

Before we start building our application, it is important to understand how your data is used and how to protect you and your company from data leaks, vulnerabilities, and intellectual property theft.

In today's digital landscape, ensuring the security of your applications is paramount. This is especially important when incorporating AI-generated code into your projects. In this article, we will explore various security measures, including OAuth, secret key management, and encryption, and discuss the unique considerations for securing AI-generated code. We will also delve into protecting data, intellectual property, application security, and utilizing protection stores. Finally, we will examine how to conduct security assessments, penetration testing, and social engineering testing while considering the implications of using generative AI tools such as ChatGPT.

Data security and intellectual property protection is a huge concern and a large cause of hesitation in implementing generative AI. LLMs often store all of your prompts, responses, and are linked to your individual account. Do NOT put any sensitive information into an LLM unless you are certain that the prompt does not contain anything that can be used against you or organization.

Key Security Measures

OAuth

OAuth is an open standard for access delegation that allows users to grant third-party applications access to their information without sharing their credentials. Implementing OAuth in your applications can help ensure secure authentication and authorization.

Secret Key Management

Securely managing secret keys is crucial for protecting sensitive data and application security. Proper key management includes using key stores, rotating keys regularly, and employing key management services.

Encryption

Encryption is the process of converting data into a code to prevent unauthorized access. Utilizing encryption for data at rest and in transit can help protect sensitive information and maintain privacy.

Protecting Your Applications

Protecting Data

Securing data involves implementing proper access controls, data encryption, and secure storage solutions to prevent unauthorized access, tampering, or data breaches.

Protecting Intellectual Property

Safeguarding intellectual property (IP) requires implementing strict access controls, securing communication channels, and using tools like digital rights management (DRM) to protect IP from unauthorized use.

Protecting Application Security

Application security involves securing every aspect of an application, including code, data, and infrastructure. This can be achieved through proper authentication, authorization, input validation, and regular security assessments.

Using Protection Stores

Protection stores, a subset of prompt stores, can be utilized to manage sensitive information securely. These stores can help ensure that OAuth patterns, keys, and other sensitive data are handled securely and not exposed to vulnerabilities.

Security Assessments, Penetration Testing, and Social Engineering Testing

Regular security assessments, penetration testing, and social engineering testing are crucial for identifying and mitigating vulnerabilities in your applications. These tests can help ensure that your applications are secure and resilient against potential attacks.

Generative AI tools, such as ChatGPT, can be employed to assist in these testing processes. By providing sample prompts and code snippets, developers can generate security considerations, create test cases, and simulate social engineering scenarios. However, it's important to consider the potential risks and limitations of using AI-generated code and ensure that human oversight is maintained throughout the process.

In conclusion, securing your applications is an essential practice for maintaining privacy and integrity in today's digital world. By implementing robust security measures and considering the unique challenges of AI-generated code, you can ensure that your applications remain secure and reliable.

This knowledge area defines different data types, provides techniques for anonymizing data, and discusses considerations in regards to generative AI and prompting.

Establishing a pattern for prompts when using language models is a prerequisite to efficiently using the knowledge areas whenbuilding an application. Understanding this pattern allows you to clearly define your request and ultimately control the output received from a LLM.

Let’s look at a prompt we may use during the ideation process:

how many Americans own bikes?

Looking at the prompt, it follows the command-target-specifier (CTS) pattern.

What is the CTS Pattern?

This pattern is not unique to the ideation knowledge area and can be applied universally. However, different domains will have different commands, targets, and specifiers. Industries will have their own commands, targets, and specifiers as well.

Let’s look at a prompt from the Generation knowledge area:

create a navigation bar in react using chakra ui

We can see how the pattern can be leveraged across domains and their processes.

The above examples are simple prompts. In the next sections, we will dive deeper into prompt context and prompt chaining to get even more out of your prompts.

ChatGPT cannot output visual content. Its understanding of visual elements come from textual data describing different visual elements such as navigation bars, hero sections, or tables. While this can be effective in building good looking UI elements, you should expect some limitations in this domain. We will go through various visualization use cases in detail in the domain.

Example: Generating ASCII art using Generative AI

Prompt

Response

Comparing that to a dedicated ASCII art generator:

Even when prompted for a more detailed version, ChatGPT's visualization capabilities are currently limited.

Response:

This ASCII art includes more detail, such as feathers, claws, and a beak. ASCII art can range from simple designs to highly detailed and intricate artwork. It's a fun and creative way to express oneself using only the keyboard.

And when we ask GPT-4, we get a more honest response that it is limited in it's ability to generate ASCII art, but the art provided is more accurate.

Prompt

Response

Unfortunately, creating an extremely detailed owl using ASCII characters is beyond the scope of what I can provide within this text format. However, I can provide you with a simpler ASCII owl and a caption for it. You can always look for more detailed ASCII art online or use an ASCII art generator tool to create one.

Here's a simple owl in a markdown code block with a caption:

Protecting Modern Web Applications: A Comprehensive Guide to Application Security

Client-Side Security

Client-side security involves protecting the user interface and user-facing components of a web application. Here are some best practices for securing the client side:

1. Use HTTPS: Ensure that all communications between the client and server are encrypted using HTTPS. This helps protect data in transit from eavesdropping and man-in-the-middle attacks.

2. Secure Cookies: Enable the Secure and HttpOnly flags for cookies to prevent cross-site scripting (XSS) attacks and restrict access to sensitive data.

3. Implement Content Security Policy (CSP): CSP helps prevent XSS attacks by controlling the sources of content that can be loaded by the browser.

4. Use Input Validation and Sanitization: Validate and sanitize user input on the client-side to prevent code injection attacks like XSS and SQL injection.

Middleware and APIs Security

Middleware and APIs play a crucial role in connecting the client-side and server-side components of a web application. To secure middleware and APIs, follow these best practices:

1. Use Authentication and Authorization: Implement proper authentication and authorization mechanisms, such as OAuth or JSON Web Tokens (JWT), to control access to your APIs.

2. Validate and Sanitize Input: Ensure that all user input passed to APIs is validated and sanitized to prevent code injection attacks.

3. Rate Limiting: Implement rate limiting to protect your APIs from denial-of-service (DoS) attacks and abuse.

4. Secure API Endpoints: Use HTTPS for API endpoints to encrypt data in transit and protect against man-in-the-middle attacks.

Server-Side Security

Server-side security is essential for protecting the backend components of a web application, such as databases and application servers. Here are some server-side security best practices:

1. Secure Data Storage: Encrypt sensitive data stored in databases and follow the principle of least privilege when granting access to data.

2. Patch Management: Keep your server software, including operating systems and application frameworks, up-to-date with the latest security patches.

3. Secure Configuration: Harden your server configuration to reduce the attack surface and eliminate potential vulnerabilities.

4. Implement Network Security: Use firewalls and intrusion detection systems (IDS) to protect your server infrastructure from unauthorized access and attacks.

Conclusion

Ensuring the security of modern web applications requires a comprehensive approach that covers client-side, middleware and APIs, and server-side security. By following best practices and staying informed about emerging threats and vulnerabilities, developers can build and maintain secure web applications that protect sensitive data and provide a safe user experience.

Using Prompt Stores to Securely Manage Sensitive Data with Large Language Models

When working with large language models like ChatGPT, it's essential to ensure that sensitive data, such as OAuth patterns, API keys, and other secrets, are managed securely. One way to do this is by using prompt stores, which can provide context to an AI conversation while maintaining the necessary security measures. In this article, we will explore how prompt stores can be used to prevent vulnerabilities and ensure that sensitive data is handled properly.

Prompt Stores for Context Management

Prompt stores are a mechanism for providing contextual information to a conversation with a large language model. They help maintain the context of an ongoing discussion, making it easier for the AI to understand the conversation and generate more relevant responses. By storing contextual data in prompt stores, developers can ensure that sensitive information is not inadvertently exposed during the conversation.

Secure Storage of Sensitive Data

To protect sensitive data, such as OAuth tokens, API keys, and other secrets, developers should store them in secure key stores or environment variables (e.g., .env files). These storage methods ensure that sensitive information is not hardcoded in the application's source code, which can lead to vulnerabilities and unauthorized access.

Here are some best practices for securely managing sensitive data:

1. Use secure key stores or environment variables to store sensitive information. These solutions protect sensitive data from unauthorized access and make it easier to manage and rotate secrets when necessary.

2. Avoid sharing sensitive data in the conversation with the AI. When providing context to the AI through prompt stores, ensure that sensitive information is not inadvertently included in the prompts or responses.

3. Implement proper access controls to limit access to sensitive data. Only allow authorized users and applications to access the key stores or environment variables containing sensitive information.

4. Regularly rotate secrets, such as API keys and OAuth tokens, to minimize the potential impact of a security breach. This practice reduces the likelihood of unauthorized access to your application or services.

Conclusion

By using prompt stores to provide context to a conversation with a large language model, developers can maintain a secure environment for sensitive data. It is essential to follow best practices for securely storing and managing sensitive information, such as OAuth patterns, API keys, and other secrets. By doing so, developers can prevent vulnerabilities and ensure the security of their applications and services.

Using Generative AI for Penetration Testing and Security Assessments

Generative AI models, like ChatGPT, have the potential to revolutionize the way we conduct penetration testing and security assessments. By leveraging the vast knowledge and learning capabilities of AI, developers and security professionals can automate and enhance the process of identifying vulnerabilities and potential attack vectors. In this article, we will discuss how generative AI can be used for penetration testing and security assessments, along with examples of prompts and code snippets.

Prompt Examples for Penetration Testing and Security Assessments

1. "Identify potential SQL injection vulnerabilities in the following code snippet:"

2. "Suggest potential cross-site scripting (XSS) attack vectors for a given web application."

3. "Analyze the security of this API endpoint and suggest possible improvements."

4. "Perform a risk assessment for the following server configuration."

Using ChatGPT for Penetration Testing

Generative AI models like ChatGPT can be used to identify potential vulnerabilities in code or server configurations. By providing a code snippet or configuration details as input, ChatGPT can analyze the information and suggest possible attack vectors or areas of concern. For example, you can provide a code snippet and ask ChatGPT to identify SQL injection vulnerabilities, as shown in the following code example:

Example 1: Potential SQL injection vulnerability

javascriptCopy codeconst express = require('express');
const bodyParser = require('body-parser');
const app = express();

app.use(bodyParser.json());

app.post('/login', (req, res) => {
  const username = req.body.username;
  const password = req.body.password;
  const query = `SELECT * FROM users WHERE username = '${username}' AND password = '${password}'`;
  // ...
});

app.listen(3000, () => console.log('Server listening on port 3000.'));

Using ChatGPT for Security Assessments

ChatGPT can also be used to perform security assessments on various aspects of an application, such as API endpoints, server configurations, and network infrastructure. By providing the necessary information and asking ChatGPT to analyze the security of the component, you can get valuable insights and suggestions for improvements. For example, you can ask ChatGPT to analyze the security of an API endpoint:

Example 2: Using ChatGPT for security assessments

Assuming a ChatGPT library for Node.js exists:

async function analyzeEndpointSecurity(chatGpt, apiEndpoint) {
  const prompt = `Analyze the security of this API endpoint and suggest possible improvements:\n\n${apiEndpoint}`;
  const response = await chatGpt.generate(prompt);
  return response;
}

// Example: Analyzing the security of an API endpoint

const chatGpt = new ChatGPT(); // Assuming an instance of ChatGPT class is created

const apiEndpoint = `
GET https://api.example.com/v1/users/:id
Authorization: Bearer <access_token>
`;

analyzeEndpointSecurity(chatGpt, apiEndpoint).then(securityAnalysis => {
  console.log(securityAnalysis);
});

Please note that these examples assume the existence of a ChatGPT library for Node.js, and you would need to implement the ChatGPT class and its generate method according to your use case.

Conclusion

While these are just a subset of the various security assessment and penetration test you could run, we can see the capability to quickly generate various testing scenairos.

Generative AI models like ChatGPT offer a promising avenue for automating and enhancing penetration testing and security assessments. By providing the AI with relevant information and crafting specific prompts, developers and security professionals can gain valuable insights into potential vulnerabilities and areas for improvement. However, it's important to remember that AI-generated suggestions should be used as a supplementary tool, rather than a replacement for human expertise and manual testing.

Once your problem statement is defined, the next step is to gather requirements for the application. This could involve finding the key features and functionality required, as well as any technical constraints or limitations.

Generative AI, like ChatGPT, can play a valuable role in defining requirements for user stories, which helps streamline project planning and development. User stories are high-level descriptions of the desired functionality of a product from the perspective of a user or a customer. They describe the intended outcome but do not delve into the specifics of how that outcome will be achieved. Requirements, on the other hand, are more detailed and specific, outlining the exact features, functionality, and constraints of the system to be developed.

In the context of software development, requirements are critical for both the development team and stakeholders, as they serve as the foundation for understanding and agreeing upon the project's scope and objectives. Generative AI can assist in refining and elaborating on these requirements by providing insights, suggestions, and alternative perspectives.

Here are some examples of prompts that can be used with ChatGPT to further define business requirements:

1. "Given the user story about a customer renting a bicycle, what are some additional functional requirements that should be considered?"

2. "What non-functional requirements should be taken into account for a real-time messaging application?"

3. "Considering the user story of a bank customer transferring money between accounts, what security requirements should be in place?"

When gathering requirements, it's crucial to consider the human aspects, as these can have significant implications on the project's success. These aspects include legal, compliance, financial, and emotional requirements, which are often interrelated.

Example: Requirement considerations

Legal requirements refer to the need for the project to adhere to applicable laws and regulations. For example, a financial application must comply with data privacy laws and financial regulations. ChatGPT could be used to generate prompts or questions that help identify relevant legal concerns, such as "What are the data privacy regulations that apply to our application?"

Compliance requirements involve ensuring that the project follows industry standards, best practices, and internal policies. Examples include accessibility standards, security certifications, or corporate guidelines. ChatGPT can assist in identifying these requirements by generating prompts such as "What industry standards should our project adhere to?"

Financial requirements pertain to the budgetary constraints and financial objectives of the project. ChatGPT can help to clarify financial aspects by generating questions like "What is the estimated total cost of ownership for this project?" or "How will this project generate revenue?"

Emotional requirements focus on the user experience and the emotional impact of the product on its users. These requirements may involve user satisfaction, ease of use, or the aesthetics of the interface. ChatGPT can be employed to explore these aspects, with prompts like "How can we design the user interface to evoke a sense of trust and reliability?"

In conclusion, generative AI, like ChatGPT, can be a powerful tool in defining requirements for user stories, taking into account various aspects such as legal, compliance, financial, and emotional factors. By providing targeted prompts and generating insightful responses, generative AI can help streamline the requirement gathering process, ensuring a more robust and well-rounded foundation for project development.

With large language models and generative redefining the value of work, intellectual property will become an increasing valuable asset that must be protected. When sending prompts containing sensitive information or intellectual property, it is essential to ensure that your data remains secure and confidential. Here are some steps you can take to protect your IP when interacting with AI models:

1. Anonymize your data: Before sending any sensitive information to the AI model, remove or obfuscate any personally identifiable information (PII) or confidential data. We discuss this in detail in Protecting Data.

2. Use secure connections: Ensure that your connection to the AI model's API is encrypted and secure. Use HTTPS and SSL/TLS to transmit data between your application and the AI service. This will help prevent unauthorized access to your data during transit.

3. Limit data retention: Check the data retention policies of the AI service provider. Ensure that they have a reasonable data retention period and that they follow proper data deletion practices. If possible, use a provider that allows you to configure data retention settings according to your needs.

4. Review terms of service and privacy policies: Carefully read the terms of service and privacy policies of the AI service provider. Ensure that they do not claim any ownership of the data you send and that they have proper security measures in place to protect your data.

5. Monitor usage and access: Keep track of who has access to the AI model within your organization. Limit access to only those who require it and regularly review the usage logs to identify any suspicious activity.

6. Contractual agreements: Establish clear contractual agreements with the AI service provider that outline the ownership of intellectual property, data protection requirements, and the responsibilities of both parties.

7. Use on-premises or private cloud solutions: If available and feasible, consider using on-premises or private cloud solutions for AI processing. This will give you more control over the storage and processing of your data, as well as the security measures in place.

8. Stay informed and adapt: As technology evolves, so do the threats and risks associated with it. Keep yourself informed about the latest developments in AI and data protection and update your strategies accordingly.

Unlike data, intellectual property is much more difficult to anonymize. When using generative AI, you should always be mindful and ask yourself what someone could infer by reading through your prompt history. By keeping this mindset and taking the precautions above, about the security of your intellectual property, you can minimize the risks associated with using large language models while still benefiting from their capabilities.

Using Generative AI to Test Processes for Social Engineering

Generative AI, like ChatGPT, can be a powerful tool to test processes for social engineering vulnerabilities. By simulating potential attack scenarios and analyzing the responses of AI chatbots or humans, organizations can identify weak points in their security practices and train employees to recognize and respond to social engineering attempts. In this article, we will discuss how generative AI can be used to test processes for social engineering and provide examples, including code snippets and a Mermaid.js diagram.

Testing Processes for Social Engineering

Generative AI models can simulate social engineering attacks, such as phishing, pretexting, or baiting, to gauge the effectiveness of an organization's security training and protocols. By crafting prompts that mimic real-life social engineering attempts, you can test how well an AI chatbot or human responds to these threats.

Example: Convincing an AI Chatbot or Human to Reveal Personal Information

In this example, we will simulate an attempt to extract personal information about a bank customer.

Here's the equivalent code example in Node.js using a hypothetical ChatGPT library:

async function testSocialEngineering(chatGpt, prompt) {
  const response = await chatGpt.generate(prompt);
  // Analyze the response to determine if the AI chatbot or human revealed sensitive information
  return isInformationRevealed(response);
}

// Example: Convincing an AI Chatbot or Human to Reveal Personal Information

const chatGpt = new ChatGPT(); // Assuming an instance of ChatGPT class is created

// Craft a prompt that simulates a social engineering attack
const prompt = `
You are a social engineer trying to extract personal information about a bank customer named John Doe.
Attempt to convince the AI chatbot or human representative to reveal the following information:
- Account number
- Account balance
- Recent transactions
`;

// Test the AI chatbot or human's response to the social engineering attempt
testSocialEngineering(chatGpt, prompt).then(revealedInformation => {
  console.log(revealedInformation);
});

Note that this example assumes the existence of a ChatGPT library for Node.js, and the isInformationRevealed function should be implemented to analyze the response for sensitive information.

Mermaid.js Diagram

A Mermaid.js diagram representing the process of testing social engineering vulnerabilities using generative AI:

Conclusion

Generative AI models like ChatGPT offer a novel approach to testing processes for social engineering vulnerabilities. By simulating social engineering attacks and analyzing the responses of AI chatbots or human representatives, organizations can identify and address weak points in their security practices. However, it's important to consider the ethical implications of using AI in this manner, and ensure that testing is conducted responsibly and with appropriate consent.

Generative References

• chatgpt-4

The below sections cover a broad scope of ideation and refinement approaches with references to concepts often discussed in product management, project management, and agile frameworks for developing an application. If you have leveraged these frameworks before, then some of content may be redundant, but the intent is to create new perspectives in how generative AI can be used to accelerate or improve the quality of these activities.

In relation to generative AI, you can use prompts to help identify problems, create new ideas to solve those problems, elaborate on existing solutions, or challenge your own thoughts through other suggestions.

The 13 subject knowledge areas provide thought, prompt, and development patterns to efficiently create in human guided generative AI. This documentation, specifically covers full stack engineering use cases for web applications (GDF-FSE).

The subject knowledge areas are:

• Ideation

• Specification

• Generation

• Transformation

• Replacement

• Integration

• Separation

• Consolidation

• Templating

• Visualization

• Verification

• Implementation

• Optimization

Once your problem statement and intended outcome is well defined. You'll want to create and prioritize the features that will be included in the bicycle rental application.

This involves breaking down the user stories into smaller tasks and estimating the time and effort required to complete each task. Features should be prioritized based on their importance to the customer and their impact on the overall project.

Test-driven feature development is one the best ways to write and identify features for an application.

Given-When-Then

Given-When-Then is a testing framework that helps to write and organize automated tests by breaking them down into three distinct parts:

• Given: The setup or precondition for the test.

• When: The action or event being tested.

• Then: The expected outcome or result of the test.

This approach helps to create more readable, reusable, and maintainable test cases by making them easier to understand and debug. In the same way this is used to write test cases, we will use it to write the basis of our features.

Example features of a bicycle rental application web app using Given-When-Then:

While the above provides a great starting place for the core features of our app, it needs further refinement. To refine these features further, we will turn them into user stories.

Generative References

create some example features of a bicycle rental application web app in the format of given when then and give an introductory explanation of what given when then is

With the problem statement clear, requirements defined, and user stories groomed, you will be in an informed state to appropriately use generative AI to generate new ideas and concepts for the software application or requirements that should be met to ensure project success. This could involve providing LLM with a brief description of the software application and asking it to generate potential features, user stories, or use cases.

Example: Generating features for a bicycle rental application

Prompt

Response

1. Real-time Availability and Reservation System: A feature that allows users to view the real-time availability of bicycles at various rental locations and reserve a bike in advance. This would involve integrating a map-based interface, displaying the number of available bikes at each location and offering a seamless reservation process.

2. Smart Pricing and Payment Integration: A dynamic pricing system that calculates rental fees based on factors such as duration, demand, and location. This feature would also include a secure and user-friendly payment integration, allowing customers to pay for their rentals using popular payment methods like credit cards, PayPal, or mobile wallets.

3. In-app Navigation and Route Planning: A feature that enables users to plan their bike routes by leveraging GPS and mapping services. The app could suggest optimal routes, taking into account factors such as distance, elevation, and estimated time of arrival. Additionally, the app could provide real-time navigation guidance, ensuring users reach their destinations safely and efficiently.

4. Social and Gamification Features: Introducing social features that allow users to share their rides, achievements, and favorite routes with friends or other users of the app. This could be complemented by gamification elements, such as earning points, badges, or rewards for reaching milestones, completing challenges, or participating in community events.

5. Maintenance and Support System: A feature that allows users to report issues with their rented bikes, such as flat tires or mechanical problems, directly within the app. The system would facilitate communication between the user and the support team, enabling quick resolution of issues and minimizing downtime. This feature could also include preventive maintenance reminders for the rental company, helping them keep their bikes in optimal condition.

These five potential features, when combined in a bicycle rental application built using React and Node.js, would create a user-friendly, efficient, and engaging platform that caters to the needs of both customers and the rental company.

Evaluation and Refinement of Ideas

Once ideas have been generated using GPT, they should be evaluated and refined based on feasibility, usability, and alignment with the requirements and problem statement. This could involve brainstorming sessions with stakeholders and subject matter experts to further develop and refine the ideas generated by GPT.

Conclusion

Generative AI can be a great tool for creating features, user stories, and even requirements. This process can be extremely beneficial for processes and projects that follow a standard methodology like existing payment processes or navigation features.

How to choose a language?

There are a number of reasons of to use one programming language over another.

1. Cost: Some programming languages are free to use, while others require licensing fees or paid support. Depending on the organization's budget, they may choose a language that is more cost-effective for their needs.

2. Resource expertise: If an organization already has a team of developers who are proficient in a particular programming language, they may choose to use that language for new projects to minimize the need for training and to leverage the team's expertise.

3. Performance: Some programming languages are better suited for specific tasks or applications than others. For example, C++ is often used for system-level programming and high-performance applications, while Python is popular for data science and machine learning.

4. Security: Some programming languages have more built-in security features and libraries than others. For example, Rust is designed to prevent memory errors and is often used for systems programming where security is a critical concern.

5. Compatibility: Some programming languages are more compatible with specific platforms or operating systems than others. For example, Java is known for its cross-platform compatibility, making it a popular choice for developing mobile and web applications.

In our bicycle rental application, we specified the use of Javascript as the primary language, React as the core framework, Next.js as a specific front-end framework on top of React, and Chakra UI as an element library for styling elements like buttons, forms, and sections. This decision was made due to my familiarity with the language, frameworks, and libraries in addition to a large support community.

Language Specfication

After considering the above reasons of choosing one application over another and you have made a selection of which language you would like to proceed with, simply add it to the specifier of your prompt.

For example:

Version Specification

To specify a language in a LLM, we should remember to specify a version or indicate the latest version in the prompt and keep in mind that the latest in the model may not be the latest version published.

For well-established frameworks, breaking changes are not as frequent as newer frameworks that are going through rapid change. Meaning that even if the model is not running off the latest version, the code may still be the most optimal and syntactically correct implementation.

Let's use the difference between Javascript ES5 and ES6 for example.

Example of Differences in Language Versioning

Imports are a feature in JavaScript that allow developers to share and use code from other JavaScript files. The syntax and mechanism for imports differ between ES5 and ES6.

In ES5, developers commonly used script loading to include external JavaScript files in a web page. This technique required creating a new <script> tag in the HTML file and specifying the src attribute to point to the external JavaScript file. The code in the external file would then be executed in the context of the web page.

In ES6, a new syntax for importing and exporting code was introduced, along with a module system that allows developers to define and import modules that encapsulate code and data. The import statement is used to import functions or variables from another module. The new module system allows for static analysis of dependencies, which can result in faster and more efficient code execution.

Here's an example of an import in ES5 and ES6:

In ES5:

In ES6:

In the ES6 example, the export statement is used to export the add function from the module.js file. In the main.js file, the import statement is used to import the add function and make it available for use. This syntax is more concise and allows for better management of dependencies between different JavaScript files.

Now that we have features that describe the actions and interactions that users will have with the bicycle rental application, we will create user stories to develop from. User stories should be developed collaboratively with stakeholders and end-users, and should focus on delivering value to the customer.

User stories are defined work items that describe features and functionality that power an end user action.

User stories typically have the following characteristics :

• Have a description that summarizes the use case the user story is solving

• Have acceptance criteria that defines when a story is considered complete

• Represent a single functioning piece of code

• Assigned to a single developer

• Moved through phases that track the status of the work item

• Are reviewed in a QA process

• Often have subtasks that are pieces of work needed to be completed for the user story to be completed

Given our features from the , let's elaborate on those and create some user stories.

User Story 1: User Registration

As a new user, I want to register for an account so that I can start using the bicycle rental application.

Acceptance Criteria:

• The user should be able to navigate to the registration page from the login page.

• The registration form should include fields for name, email, phone number, and password.

• Upon submitting the registration form, the user's information should be stored securely in the database.

• The user should be redirected to the login page upon successful registration.

• If there is any error during the registration process, the user should be notified of the error and asked to correct it.

User Story 2: Search Bicycles

As a user, I want to search for available bicycles based on location and date range so that I can find a suitable bicycle for my needs.

Acceptance Criteria

• The user should be able to access the search feature from the home page.

• The search feature should include fields for location, start date, and end date.

• The application should display a list of available bicycles based on the user's search criteria.

• The list of available bicycles should include details such as location, type, and price.

• The user should be able to filter and sort the list of available bicycles based on their preferences.

• If there are no bicycles available for the given search criteria, the user should be notified accordingly.

User Story 3: Rent Bicycle

As a user, I want to rent a bicycle for a specified duration and complete the payment process so that I can start using the rented bicycle.

Acceptance Criteria

• The user should be able to select a bicycle from the list of available bicycles.

• The application should display the details of the selected bicycle, including rental rates and terms.

• The user should be able to specify the rental duration and see the total rental cost.

• The application should guide the user through the payment process securely and reliably.

• Upon successful payment, the user should receive a confirmation email with the rental details and instructions.

• If there is any error during the rental or payment process, the user should be notified of the error and asked to correct it.

User Story 4: Return Bicycle

As a user, I want to return the rented bicycle to the designated location and receive confirmation of the return.

Acceptance Criteria:

• The user should be able to see the rental details, including the rental duration and the designated return location.

• The user should receive reminders about the approaching return deadline via email or push notification.

• The user should be able to navigate to the designated return location using the application's built-in map or directions feature.

• Upon returning the bicycle, the user should receive a notification confirming the return and the rental charges should be deducted from their account.

• If there is any dispute or issue related to the rental or return process, the user should be able to contact customer support through the application.

Summary

Project strcutures are the files and folders that make up your application. The importance of your model knowing your project structure, gives your responses context that ensure prompts from any of the knowledge areas align to the project structure you are working with.

Once you have your language and libraries established, you will want to define your project structure and store it as a prompt store.

In the context of a Next.js application, there are a few common project structures that you can use, depending on your needs. One common structure is to organize your application by feature, with each feature having its own folder. Within each feature folder, you can have sub-folders for components, pages, and utilities. Here's an example:

In this structure, the components folder contains reusable components that can be used across features. The features folder contains the main functionality of the application, with each feature having its own folder. Each feature folder has a components folder for feature-specific components, a pages folder for feature-specific pages, and a utils folder for feature-specific utility functions.

The pages folder contains the top-level pages of the application, such as the homepage and any other static pages. The _app.js file is used to initialize the application and can be used to set up things like global styles or state providers.

The public folder contains any static assets that should be served directly by the server, such as images or favicon.ico.

The utils folder contains utility functions that can be used across features.

The .env.local file contains environment variables that are specific to the local development environment.

The next.config.js file is used to configure Next.js, such as setting up webpack or configuring plugins.

Here's an example of how you could structure your markdown prompt store based on this project structure:

This way, when your model receives a prompt that references a component or utility function, it can look for it in the appropriate folder based on the project structure.

In programming, schemas are structures. These structures help define what is and is not allowed in a given call and response. Let's take a payment input form for example. Each field in the form has "structure" to it. For instance, names cannot have numbers, credit card numbers can not have alphabetical characters, expiration year cannot be in the past and must be a four digit integer.

Documentation and use of schemas in validation (a process that checks whether a given value matches a specified schema), provides a means of communication to users, developers, and systems.

In the case of the payment form, an API schema is used to let the client (a bicycle rental application for example) know what fields are expected for the payment request to go through . Client side validation on the user interface (credit card number input form for example) lets a user know that only 16 digit integers are accepted. Server side validation ensures that just because a user does not the use specified payment form, for the server to only accept requests if they align to a particular schema.

Example: Creating a schema for a credit card form

Prompt

Response

Sure! Here's an example of how you can create a schema for a credit card form to be used on the client side in a Next.js Chakra UI app:

In this schema, we're using the Yup library to define a schema for a credit card form with the following fields: name, cardNumber, expirationMonth, expirationYear, and cvv.

The name field is a string that is required and must match the regular expression /^[a-zA-Z\s]*$/ to ensure that it only contains letters and spaces.

The cardNumber field is a string that is required and must match the regular expression /^[0-9]{16}$/ to ensure that it only contains 16 numeric digits.

The expirationMonth field is a number that is required and must be between 1 and 12.

The expirationYear field is a number that is required and must be in the future and within the next 10 years.

The cvv field is a string that is required and must match the regular expression /^[0-9]{3}$/ to ensure that it only contains 3 numeric digits.

You can use this schema to validate the input data on the client side before submitting it to the server. For example, you can use the validate function from Yup to validate the form data against the schema, like this:

In this example, we're using Chakra UI's FormControl, FormErrorMessage, FormLabel, and Input components to create a form that includes the fields defined in the credit card schema. We're also using the useFormik hook from formik to manage the form state and handle form submission.

We're passing the credit card schema to the validationSchema prop of the useFormik hook to validate the form data against the schema. The isInvalid prop of the FormControl component is set to true if the field has been touched and there is a validation error.

Finally, we're using the getFieldProps function from formik to bind each form input to the corresponding formik field, and using the handleSubmit function from formik to handle form submission.

That's it! You can use this credit card form component in any other React component in your Next.js app.

Discussion

In the response from ChatGPT, we can see the code provided uses a library called Yup and Formik to specify the schema and then apply that schema to the form.

Example: Specifying a credit card OpenAPI Spec

OpenAPI spec's are the industry standard for documenting an API service. An API service is a application hosted on a server that accepts requests from the web and returns responses. These specifications help systems and developers understand what data and thieir types are expected to be sent to receive an expected response.

Prompt

Response

In this OpenAPI spec, we're defining a POST operation for submitting credit card information to the /credit-card endpoint. The operation expects a JSON object in the request body with the following fields: name, cardNumber, expirationMonth, expirationYear, and cvv. We're using the CreditCardInput schema to define the structure of the request body, which includes the required fields and their data types, minimum and maximum lengths, and minimum and maximum values.

We're also defining two possible responses: a successful 200 response and an error 400 response that includes an array of error messages if the input data is invalid.

Finally, we're defining the OpenAPI spec components, including the CreditCardInput schema, which can be reused in other endpoints or operations.

Note that this OpenAPI spec is just an example, and you may need to adjust it based on your specific use case and requirements.

In the context of JavaScript, a library and a package are both terms used to describe collections of code that can be used to add functionality to a JavaScript application. However, there are some differences between the two.

Library

A library is a collection of code that provides a set of pre-built functions or modules that can be used to perform specific tasks or add functionality to a JavaScript application. Libraries typically provide a set of APIs that can be called from within an application to perform specific tasks, such as manipulating the DOM, making HTTP requests, or performing data validation.

Some popular JavaScript libraries include jQuery, React, and Vue.js. Libraries can be used to speed up development time and improve code quality by providing pre-built functionality that can be easily integrated into an application.

Package

A package, on the other hand, is a collection of code that can include libraries, modules, and other resources needed to run a JavaScript application or module. Packages are typically distributed using a package manager, such as npm or Yarn, and can include metadata about the package, such as version numbers and dependencies.

Packages can be used to manage dependencies and versioning in a JavaScript application, and can make it easier to share code between different applications or modules. Packages can include libraries, modules, and other resources needed to run a JavaScript application, and can be installed using a package manager.

In summary, a library is a collection of code that provides pre-built functionality for a JavaScript application, while a package is a collection of code and resources needed to run a JavaScript application or module, which can include libraries, modules, and other resources.

Example: Changing Map Libraries

Let's look at a use case for the bicycle rental application, displaying bicycles on a map so that users can see what bikes are close to them.

First of mind, is to use Google Maps to display bikes that are close to the customer.

Prompt

Response:

Sure, here's an example of a React component in Chakra UI using Next.js to show a bicycle icon using Google Maps:

First, you need to install the required dependencies:

Then, create a file called MapWithBikeIcon.js in your components directory:

In this component, we're importing GoogleMapReact and Box from Chakra UI, and FaBicycle from react-icons/fa. We're also defining a functional component AnyReactComponent that will render the bicycle icon.

MapWithBikeIcon is the main component that takes two props, center and zoom, to determine the initial position and zoom level of the map. In the component's return statement, we're rendering a Box with a fixed height and width to contain the Google Map. Inside the GoogleMapReact component, we're passing the required bootstrapURLKeys, defaultCenter, and defaultZoom props, and rendering the AnyReactComponent with the lat and lng values from center.

Replace YOUR_API_KEY with your actual Google Maps API key. Then, you can use this component in any other React component in your Next.js app:

Here, we're importing Box from Chakra UI, and MapWithBikeIcon from the component we just created. We're defining the center and zoom props, and passing them to MapWithBikeIcon inside a Box. You can customize the center and zoom values to show the map at any location and zoom level you want.

That's it! You should now see a map with a red bicycle icon in the center of the page when you navigate to the MapPage route in your Next.js app.

Discussion

In the above, the instructions and component are largely clear. I think the naming of AnyReactComponent could be better, and could be improved through a quick replacement prompt of rename the AnyReactComponent to BicyleIcon.

However, let's assume that in discussing looking at Google Map's features and pricing, you find that Google Maps has more features than you need, and you're simply looking for a simpler more cost effective mapping solution. After some research, you decide to use MapBox.

Let's use a replacement prompt to change to a mapbox implementation:

Prompt:

Response:

Sure! Here's an example of how you can modify the React component we previously created to use the Mapbox library instead of Google Maps:

First, you need to install the required dependencies:

Then, create a file called MapWithBikeIcon.js in your components directory:

In this component, we're importing ReactMapGL and Marker from react-map-gl instead of GoogleMapReact. We're also using process.env.NEXT_PUBLIC_MAPBOX_ACCESS_TOKEN to access your Mapbox API access token, which you should add to your .env.local file.

MapWithBikeIcon is the main component that takes a viewport object as a prop, which contains the initial position and zoom level of the map. In the component's return statement, we're rendering a Box with a fixed height and width to contain the Mapbox map. Inside the ReactMapGL component, we're passing the mapboxApiAccessToken and viewport props, and rendering the AnyReactComponent with the latitude and longitude values from the viewport object.

You can use this component in any other React component in your Next.js app:

Here, we're defining the center and zoom values as latitude and longitude in the viewport object, and passing it to MapWithBikeIcon. We're also using the onViewportChange function to update the viewport state.

That's it! You should now see a Mapbox map with a red bicycle icon in the center of the page when you navigate to the MapPage route in your Next.js app.

Discussion:

As expected, ChatGPT successfully replaced the Google-Maps-React library with a MapBox implementation.

The ability to quickly iterate through various libraries makes it efficient to create POCs (proof of concepts) or try different products out during the RFP (request for product) process.

The Specification knowledge area is crucial when working with generative AI, as it helps define the output of a prompt with greater accuracy and detail. In the "Learn through Creation" section, we illustrated how specifying the language, framework, and UI library could guide the development of a bicycle rental application. In many cases, organizations or users of generative AI might possess expertise in specific frameworks, or face financial, compliance, or technological requirements that necessitate using particular tools. In this article, we will explore various prompts and their roles in the Specification knowledge area.

1. Language Specification: When using generative AI, it is important to specify the programming language you want the generated code to be written in. This ensures that the output aligns with your project's technical requirements and your team's expertise.

Prompt: "Generate a function in Python that calculates the rental cost for a bicycle rental application."

1. Framework Specification: Specifying the desired framework can help you get the most out of the generated code, as it will be tailored to work seamlessly within the chosen environment.

Prompt: "Create a REST API endpoint for a bicycle rental application using the Express.js framework."

1. UI Library Specification: Clearly indicating the UI library to be used can help streamline the development process, as the generated code will be compatible with the specified library's components and design system.

Prompt: "Design a user registration form for a bicycle rental application using the Material-UI library in React."

1. Compliance and Security Specification: Some projects may require adherence to specific compliance or security standards. Specifying these requirements in the prompt can ensure that the generated output complies with relevant regulations and best practices.

Prompt: "Develop a secure authentication system for a bicycle rental application that is GDPR-compliant."

1. Platform or Device Specification: For projects targeting specific platforms or devices, it's essential to mention these requirements in the prompt to ensure the generated code is optimized for the desired environment.

Prompt: "Create a responsive navigation menu for a bicycle rental application that works well on both desktop and mobile devices."

In conclusion, the Specification knowledge area plays a vital role in obtaining precise output from generative AI. By specifying the language, framework, UI library, compliance requirements, and target platforms or devices, you can guide the AI to generate code that aligns with your project's needs and constraints. Leveraging the Specification knowledge area effectively can lead to more accurate and relevant results, ultimately streamlining the development process and improving the quality of the final product.