// Code

Prompt Engineering for Code Generation

Getting reliable output instead of confident-sounding bugs

AI-generated code fails in a specific, predictable way: it looks correct, compiles or runs at first glance, and then breaks on an edge case or calls a function that doesn't exist. Most of that isn't a model limitation — it's missing context in the prompt. Here's what actually moves the needle.

State the environment explicitly

Language version, framework version, and runtime matter more for code than for almost any other prompt type. "Write a Python script" versus "write a Python 3.11 script using only the standard library, no external dependencies" produce very different reliability.

Write a function in Python 3.11 that reads a CSV file and returns rows as dictionaries. Use only the standard library (no pandas). Include type hints and handle the case where the file doesn't exist by raising a clear error, not a bare exception.

Show, don't just describe, your existing code style

If you're adding to an existing codebase, paste a short snippet of your actual code as an example before asking for new code in the same style. Models pattern-match far better from an example than from a description like "use my usual style."

Ask for the failure modes, not just the happy path

By default, models tend to generate code for the case where everything goes right. Explicitly ask what happens on bad input, empty results, network failure, or concurrent access if those matter for your use case — otherwise you'll often get code that works in the demo and breaks in production.

Break large tasks into steps

Asking for an entire feature in one prompt tends to produce code that's individually reasonable but doesn't fit together well — inconsistent naming, mismatched assumptions between functions. Better results usually come from asking for one function or module at a time, reviewing it, then asking for the next piece with the previous one as context.

Ask it to flag uncertainty

A useful pattern: explicitly instruct the model to say "I'm not certain this API exists / this is the current syntax" rather than presenting a guess with full confidence. This won't eliminate hallucinated methods entirely, but it surfaces the riskiest parts of the output so you know what to verify first.

Before writing the code, list any libraries or APIs you're not fully certain about. Then write the implementation. If you're guessing at a method name or signature, say so in a comment.

Use PromptLab's code template

The code category in PromptLab's template library builds these patterns in by default — environment specification, style-matching, and edge-case prompts — so you're not rebuilding this structure from scratch every time you need working code fast.

TRY THE CODE TEMPLATES →