Most developers treat AI coding assistants like search engines. They type a vague request, get mediocre code, and blame the tool. The difference between frustration and a tenfold speedup comes down to how you structure the conversation. Good prompting is not magic. It is a disciplined way of handing the model the exact context it needs to act like a senior engineer sitting beside you.
Generating Boilerplate and Standard Functions
Stop asking for "a REST API." Ask for a FastAPI endpoint that accepts a JSON payload, validates it with Pydantic, returns a 422 on failure, and logs the request ID to a structured logger. Specify the framework, the validation library, the error format, and the logging standard in a single prompt. When you need CRUD operations, paste your database schema or ORM models first. Then ask for the repository layer, the service layer, and the router separated into three files. The model will mirror your actual types instead of inventing generic ones.
Using AI as a Debugging Assistant
Paste the full stack trace. Paste the relevant function. Paste the test that fails. Tell the model what you expected versus what happened. A prompt like "Here is a NullPointerException at line 42. The user object should be loaded from the session middleware. Why is it null?" forces the model to trace the data flow. It will often spot a missing middleware registration or a race condition in async code that you missed after staring at the screen for an hour. Treat the model as a rubber duck that talks back with compiler knowledge.
Writing Comprehensive Unit Tests
Don't ask for "tests." Ask for Pytest cases that cover the happy path, empty collections, null inputs, boundary values, and the specific exception types your code raises. Include a fixture that mocks the external payment gateway. Request parameterized tests for the validation logic so one function exercises ten input combinations. If you use Jest, ask for describe blocks that mirror your module structure and beforeEach hooks that reset the in-memory database. The output becomes a safety net you can commit without rewriting.
Refactoring and Explaining Legacy Code
Drop a 300-line function into the chat. Say: "Rewrite this using SOLID principles. Extract the payment calculation into a strategy pattern. Move the email logic to a separate service. Add docstrings that explain why each class exists, not just what it does." The model will break the monolith into small classes with single responsibilities. It will also surface hidden coupling, like a hardcoded tax rate buried in a private method. You get modern architecture and living documentation in one pass.
Start small. Pick one task today. Write the prompt like you are briefing a contractor. Review the output. Refine the prompt. The compounding returns arrive fast.
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