01 · The tools
Which AI tools to use for coding
GitHub Copilot
VisitAutocomplete and chat inside your editor, across most languages
Cursor
VisitAn AI-native editor that understands your whole codebase for larger changes
Claude
VisitExplaining, debugging and refactoring code with strong reasoning, including Claude Code in the terminal
v0
VisitGenerating UI components and front-end code from a description
02 · The method
Step by step
- 1
Give it real context
Share the relevant code, the language and framework, and what you are trying to do. Vague prompts get generic, wrong code.
- 2
Ask for small, specific changes
Request one function or one fix at a time. Large one-shot generations are hard to review and hide bugs.
- 3
Read every line before accepting
Treat AI code like a pull request from a fast but careless junior. If you cannot explain a line, do not accept it.
- 4
Make it debug from the error
Paste the exact error message and stack trace. AI is strong at diagnosing a real error, weak at guessing without one.
- 5
Test everything it writes
Run the code and the tests. AI produces confident, plausible bugs, so working code is proven by running, not by looking right.
03 · Use this now
Copy-paste prompt for a coding task
You are a senior [language] engineer. Here is the relevant code and context: [paste code, framework, and what it does]. I want to [specific task, e.g. add validation to this function]. Write the change, explain what it does and why, and call out any edge cases, assumptions or trade-offs. Keep it minimal and idiomatic for this codebase. If my approach is flawed, say so before writing code. Do not change anything I did not ask you to touch.
04 · Avoid these
Common mistakes
- Accepting code you do not understand. You own and must maintain it; 'the AI wrote it' is not a defense for a bug.
- Pasting secrets, API keys or proprietary code into a public tool. Use an enterprise plan or keep them out.
- Trusting AI on architecture. It is strong at local code and weak at system design; the big decisions stay yours.
05 · Questions
Frequently asked questions
What is the best AI for coding?
GitHub Copilot and Cursor lead for in-editor coding, Claude and ChatGPT for explaining, debugging and larger reasoning tasks, and v0 for generating UI. Many developers pair an in-editor assistant with a chat model for harder problems.
Can AI write code for me?
Yes, and it is excellent at boilerplate, tests and debugging from an error. But it produces confident bugs, so you must review, understand and test every line. Treat it as a fast collaborator you always check, not an autopilot.
Can beginners use AI to learn coding?
Yes, if you use it to explain rather than just to produce. Ask it why code works and to walk through errors. The risk is accepting working code you do not understand, which feels productive but teaches you nothing.