This guide to getting started with ai coding assistants explains the core concepts, practical decisions, and common mistakes to watch for. Use the steps below as a starting point, then verify details against the tools and requirements in your own workflow.
Start with a small, understandable task
Ask for an explanation of one function, a draft unit test, or a refactor with a clear acceptance criterion. Keep changes small enough to review. Large prompts that ask for an entire application make it harder to spot assumptions and regressions.
Give context without exposing secrets
Share relevant files, interfaces, error messages, and constraints. Remove API keys, credentials, customer data, and internal secrets. Check the tool’s context and data policies before connecting a repository or granting access to private code.
Run the code and inspect the diff
Treat suggestions as untrusted changes. Read the diff, run tests, inspect dependencies, and check error handling. An assistant can produce code that looks plausible but calls a nonexistent API or introduces a subtle security bug.
Practical checklist
A good loop is explain, propose, review, test, and commit only after the change passes your checks.
Try it yourself
Choose one small coding task, such as explaining a function or drafting a unit test. Give the assistant the relevant code and acceptance criteria, review the diff, run the tests, and check for invented APIs or unsafe dependencies. Keep the task small enough that you can explain every change before accepting it.
Editorial note: This educational guide explains general methods and does not claim independent product testing. Check official documentation for current features, pricing, and terms before making decisions.
