Coding

Code generation

Turn requirements into working code.

Knowledge cards
Best practice
Ask for Tests Alongside the Code
A habit of requesting unit tests in the same turn as the implementation — including edge cases — so you can verify the generated code instead of eyeballing it. When a test does fail, hand the failure back for a focused [debugging](/ai-hub/chatgpt/coding/debugging/) pass.
L2 · Context engineering
Knowledge card
Generate a Function From a Clear Signature and Test
A generation move: hand ChatGPT the function signature and a couple of input/output examples, and let it write the body — instead of describing the behaviour in vague prose.
L2 · Context engineering
Mistake
Generate in Small Units, Not a Whole App at Once
A scoping rule: ask ChatGPT for one testable unit at a time — a function, a component, a module — and verify it before moving on, rather than requesting an entire app in a single prompt.
L2 · Context engineering
Knowledge card
Give ChatGPT Your Stack and Conventions Up Front
A prompting habit: before asking for code, state your language, framework and versions, plus the conventions the code must follow — so what comes back fits your project instead of a generic template.
L2 · Context engineering
ChatGPT code generation

Specify the shape before the code

The quality of generated code tracks the quality of the brief. Give ChatGPT the function signature, the input and output types, and the constraints it must respect, and it produces code that fits. Leave those out and it invents assumptions — a different data shape, a library you do not use, an error-handling style that clashes with your codebase. The most reliable pattern is to hand it a clear signature and a sentence about where the code runs, then ask for the implementation.

Generate in reviewable pieces

Asking for a whole feature in one shot produces a wall of code you cannot easily trust. Better to generate one layer at a time — the data model, then the logic, then the interface — checking each before moving on. For anything with real logic, use a reasoning model so ChatGPT works the problem through rather than pattern-matching. Small, verified steps beat a large block you have to reverse-engineer to review.

Frequently asked
How do I get ChatGPT to write code that fits my project?

Give it the signature, the types, and the constraints — including where the code runs and which libraries you use. That context stops it inventing assumptions that clash with your codebase.

Why does ChatGPT generate code with the wrong assumptions?

Usually because the prompt did not specify them. Without the data shape, dependencies, and error-handling style, it fills the gaps with defaults that may not match yours. State them up front.

Should I generate a whole feature at once?

No — generate one layer at a time and verify each before continuing. Reviewable pieces are more trustworthy than a single large block, and a reasoning model helps on logic-heavy parts.