Most developers use AI coding assistants wrong.
They type "write me a FastAPI endpoint" and get a generic response they need to rewrite anyway. After a year of using Claude and ChatGPT daily for production Python projects, I've developed a framework that consistently produces usable code on the first try.
The 3-Layer Framework
Every effective AI prompt for development has three layers:
Layer 1: Context
Tell AI about your project, stack, and constraints. This is the part most people skip entirely.
Layer 2: Task
Be specific about what you need. Not "write an endpoint" but "write a POST /invoices endpoint that validates input with Pydantic v2, creates a record with line items, and returns 201."
Layer 3: Output Format
Do you want just code? Code with explanation? A code review? All three? Say it explicitly.
Bad vs. Good Example
❌ Bad: "Write me a FastAPI endpoint"
✅ Good: "I'm building a SaaS invoicing app with FastAPI 0.109+, SQLAlchemy 2.0 (async), and PostgreSQL. I need a POST /invoices endpoint that: 1) Validates input with Pydantic v2, 2) Creates an invoice record with line items, 3) Returns 201 with the invoice object. Use dependency injection for the DB session. Include type hints and docstrings."
The good prompt produces code I can use with maybe 10% modifications. The bad prompt produces code I need to rewrite 60%.
The DEBUG Protocol
For debugging, I use a systematic approach:
- Describe the expected vs actual behavior
- Environment details (Python 3.12, FastAPI 0.109, etc.)
- Backtrack — what changed recently?
- Unit of code — paste only the minimal reproducible example
- Give the full traceback
This turns a 30-minute debugging session into a 5-minute one.
Generate → Critique → Refine
Never accept the first AI output. Use this 3-step cycle:
- Generate: "Build X with Y requirements"
- Critique: "Now review this code for security and performance issues"
- Refine: "Apply those fixes and also add error handling"
AI is surprisingly good at critiquing its own output. The second pass catches things like missing input validation, N+1 queries, and hardcoded secrets.
Role-Based Prompting
Assign AI a specific expert role for better output:
- "As a security auditor..." — for vulnerability reviews
- "As a database performance engineer..." — for query optimization
- "As a Python developer who prioritizes readability over cleverness..." — for clean code
The specificity of the role shapes the entire response.
More Where This Came From
I compiled 50+ prompts like these into a free PDF toolkit — covering project scaffolding, API development, database optimization, testing, Docker deployment, and a one-page cheat sheet.
Grab it here: AI-Powered Python Development Toolkit
It's free ($0+). I'm a Python developer in Berlin building tools for other developers. This is my first digital product and I'd love to hear what resonates and what's missing.
What's your most useful AI prompt for Python development? Drop it in the comments — I'm always expanding my toolkit.
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