Best Practices for Using AI as a Coding Assistant
A practical workflow for using AI to explain code, generate tests, debug errors, and improve developer productivity.
Give the assistant enough context
Code questions need context. Include the language, framework, relevant files, expected behavior, actual behavior, and the exact error message. If you only paste a short snippet, the assistant may guess incorrectly about the surrounding system.
Ask for small changes
AI-generated code is easier to review when the change is small. Ask for one function, one test case, or one explanation before asking for a larger refactor. Smaller changes reduce hidden bugs and make the review process faster.
Always run tests
A plausible answer is not the same as a correct implementation. Run the test suite, add focused tests for changed behavior, and inspect edge cases. AI can help write tests, but developers remain responsible for correctness.
Use AI for explanation and debugging
AI is strong at explaining unfamiliar code, translating error messages into likely causes, and suggesting debugging steps. Ask it to identify assumptions and list what evidence would confirm or disprove each hypothesis.
Protect secrets and proprietary code
Never paste API keys, private credentials, customer data, or confidential source code into tools unless you have permission and understand the data policy. Redact secrets and replace private values with placeholders.