Schemas

Aligning to well defined schema for clients and backends using AI

Summary

By employing prompt stores in generative AI, developers can maintain context regarding specific libraries or configurations, such as "bicycle-app-config," to streamline the process of generating consistent schema records in databases. This approach enhances code maintainability, readability, and reduces the likelihood of errors when accessing data.

Example: Creating an Object Schema in MongoDB using ChatGPT

A developer wants to create consistent schema records for their MongoDB database while utilizing the "bicycle-app-config" context. They can provide a template prompt to the generative AI model like ChatGPT, which outlines the desired structure and standard fields for the schema.

Prompt:

chatgpt-3.5

Generate a MongoDB schema definition using Mongoose that includes the following standard fields: id, createdDate, lastModifiedDate, createdBy, and lastModifiedBy. The schema should automatically handle the createdDate and lastModifiedDate fields. Consider the bicycle-app-config context.

Response:

const mongoose = require('mongoose');
const { Schema } = mongoose;

const BaseSchema = new Schema(
  {
    _id: {
      type: Schema.Types.ObjectId,
      auto: true,
    },
    createdDate: {
      type: Date,
      default: Date.now,
    },
    lastModifiedDate: {
      type: Date,
      default: Date.now,
    },
    createdBy: {
      type: String,
      required: true,
    },
    lastModifiedBy: {
      type: String,
      required: true,
    },
  },
  {
    timestamps: {
      createdAt: 'createdDate',
      updatedAt: 'lastModifiedDate',
    },
  }
);

module.exports = BaseSchema;

Discussion

Utilizing prompt stores in generative AI can enhance the development process and bring unity and standardization to the output:

Pros:

1. Streamlines the process of generating code by maintaining context and reducing redundancy.

2. Enhances code maintainability and readability by ensuring consistency.

3. Reduces the likelihood of errors when accessing data.

Cons:

1. Requires careful management of the prompt store context to avoid confusion or unintended consequences.