Generative Development Framework
GDF.ai
  • Intro to GDF-FSE
    • Generative AI, Large Language Models, ChatGPT?
    • Knowledge Areas
    • Access a Chat Based LLM
    • Why GDF?
    • Expectations
  • Limitations
  • Prompting
    • Prompt Patterns
    • Prompt Context
    • Prompt Stores
    • Prompt Operators
    • Prompt Chaining
  • Security
    • Protecting Data
    • Protecting Application Security
    • Protecting Intellectual Property
    • Protection Stores
    • AI Security Assessments and Penetration Testing
    • Social Engineering Testing with AI
  • Subject Knowledge Areas
    • Ideation
      • Identifying a Problem Statement
      • Plan and Prioritize Features
      • Develop User Stories
      • Requirement Gathering
      • Ideation Prompting
      • Ideation Template
    • Specification
      • Specifying Languages
      • Specifying Libraries
      • Specifying Project Structures
      • Specify Schemas
      • Specifying Elements
      • Specifying API Specs
    • Generation
      • Generating UI Elements
      • Generating Mock Data
      • Generating Schemas
      • Generating Parsers
      • Generating Databases
      • Generate Functions
      • Generate APIs
      • Generate Diagrams
      • Generating Documentation
    • Transformation
      • Converting Languages
      • Converting Libraries
    • Replacement
      • Replacing Functions
      • Replacing Data Types
    • Integration
      • Connecting UI Components
      • Connecting UI to Backend
      • Connecting Multiple Services Together
      • Connecting Cloud Infrastructure (AWS)
    • Separation
      • Abstraction
      • Model View Controller (MVC)
    • Consolidation
      • Combining UI Elements
      • Deduplicating Code Fragments
    • Templating
      • Layouts
      • Schemas
      • Project Structures
      • Content Management Systems
    • Visualization
      • General Styling
      • Visual Referencing
      • Visual Variations
    • Verification
      • Test Classes
      • Logging and Monitoring
      • Automated Testing
      • Synthetic Monitoring
    • Implementation
      • Infrastructure
      • DevOps / Deployment
    • Optimization
      • General Optimization
      • Performance Monitoring
      • Code Review
  • Guidance
    • Business Process
    • Regulatory Guidance
  • Generative Pipelines
  • Troubleshooting
    • Client Side Troubleshooting
    • Server Side Troubleshooting
    • Troubleshooting with AI
    • Documentation
    • Infrastructure Engineering
  • Terminology
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  • Generative AI Approaches
  • Human-Driven Approaches

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Troubleshooting

Patterns for troubleshooting issues when developing

While generative AI has made significant strides in simplifying software development and troubleshooting, there are instances where it may not be able to resolve issues effectively. It is essential for developers to have a strong understanding of the concepts and syntax behind their applications to efficiently resolve problems and support their applications once in production. In this article, we will explore both generative AI and human-driven approaches to solving issues in full stack web applications.

Generative AI Approaches

  1. Error Detection: Generative AI can analyze code to detect syntax errors, missing dependencies, or other common issues, and provide suggestions to resolve them. This can help developers quickly identify problems and implement fixes.

  2. Code Refactoring: Generative AI can suggest code refactoring to improve code quality, performance, and maintainability, reducing the likelihood of issues arising during development or production.

  3. Automated Testing: Generative AI can generate unit tests, integration tests, and end-to-end tests based on application requirements, ensuring that potential issues are caught early in the development process.

Human-Driven Approaches

  1. Debugging: Developers should have a strong understanding of debugging tools and techniques, such as setting breakpoints, stepping through code, and analyzing variables, to identify and resolve issues efficiently.

  2. Code Reviews: Peer code reviews can help catch potential issues, provide constructive feedback, and share knowledge among team members. This collaborative process encourages better code quality and reduces the likelihood of issues going unnoticed.

  3. Performance Profiling: Developers can use performance profiling tools to analyze the execution of their application, identify bottlenecks, and optimize performance.

  4. Logging and Monitoring: Implementing robust logging and monitoring systems can provide valuable insights into application behavior, helping developers identify and resolve issues quickly.

  5. Documentation: Maintaining clear and up-to-date documentation can help developers understand the system's architecture and dependencies, making it easier to troubleshoot issues and implement changes.

  6. Community Support: Developers can leverage community resources such as forums, blogs, and Stack Overflow to seek guidance and solutions to issues they encounter during development.

Both generative AI and human-driven approaches have their roles to play in troubleshooting web applications. Generative AI can automate error detection, code refactoring, and testing, while human-driven approaches such as debugging, code reviews, performance profiling, logging, monitoring, documentation, and community support remain essential. By combining the strengths of generative AI with the expertise and experience of human developers, teams can efficiently resolve issues and support their applications throughout their lifecycle.

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Last updated 3 months ago

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