Prompting AI for Backend API
Your backend API is the bridge between frontend and database. It exposes endpoints that the frontend calls to create, read, update, and delete data. AI can generate complete, working API servers — your job is to prompt effectively and review the results.
CRUD Operations
Most data operations fall into four categories, often called CRUD:
- Create — add new items (HTTP POST)
- Read — retrieve items (HTTP GET)
- Update — modify existing items (HTTP PUT or PATCH)
- Delete — remove items (HTTP DELETE)
Each operation maps to an HTTP method. A todo API might have endpoints like GET /todos to list all, POST /todos to create one, PUT /todos/123 to update item 123, and DELETE /todos/123 to remove it.
RESTful Design
REST is a convention for organizing APIs. Resources (like todos) get their own URL path. HTTP methods indicate the action. Responses use JSON format. Following REST conventions makes your API predictable and easier for others (and AI) to understand.
Crafting Your Prompt
When asking AI to generate a backend, be specific about:
- Framework — Flask, Express, FastAPI, etc.
- Endpoints — exactly what URLs and methods you need
- Data format — what fields requests and responses contain
- Database — SQLite, PostgreSQL, etc.
- Requirements — error handling, validation, CORS
A detailed prompt produces better code:
"Create a Python Flask API for a todo app with these endpoints: GET /todos returns all todos as JSON, POST /todos creates a new todo from {text: string}, PUT /todos/:id updates a todo with {text?: string, completed?: boolean}, DELETE /todos/:id removes a todo. Use SQLite. Include error handling for 404 (not found) and 400 (bad request). Enable CORS for frontend access. Add comments explaining each part."
Reviewing Generated Code
Don't blindly trust AI output. Check that each endpoint does what you specified. Verify input validation exists — what happens if someone sends empty text? Confirm error responses are helpful. Test that CORS headers allow your frontend to connect.
Look for the database connection setup. Does it create tables if they don't exist? Does it handle connection errors? These details matter for a working application.
Running and Testing
AI should include instructions to run the server locally. Typically you'll install dependencies, then run a command like python app.py or node server.js. Test each endpoint using your browser's dev tools, curl commands, or tools like Postman.