Two common starting points for writing code with AI are Cursor and GitHub Copilot. Cursor ties an AI editor to your project files, so you can inspect each change it makes. Copilot drafts and completes code as you type, which speeds up getting started and reading unfamiliar code. Both are freemium with a free plan you can try first. What does not change: you review the diff, run the tests, and check the security of every change the AI proposes.
Decide whether you want AI to edit files or complete your typing
Sort out the two modes before you pick a tool. If you want AI to touch multiple files in your project while you review each change, Cursor fits better, since it is built for developers who will inspect each change rather than skip review. One of its stated limits: automated edits can introduce defects, so don’t hand command execution and code changes entirely to an automated flow, and keep secrets and production systems safeguarded. If you’d rather get completions and drafts while you write, and understand someone else’s code along the way, GitHub Copilot is the easier reach, though its suggestions can carry logic or security faults you have to check line by line.
Run one small task on the free plan first
Both tools have a free plan, so don’t agonize over which tier to buy up front. Start with a small change you know well: ask Cursor to edit a single function, or let Copilot complete a snippet whose correctness you can judge at a glance. That lets you learn each tool’s behavior at low risk. One caveat: access and features vary by plan and environment for both, so what the free tier can do and how much you get may shift with the plan. Don’t treat one session as a long-term guarantee. (Pricing per the official site, verified 2026-07-15: both are freemium with a free plan; tiers are per the vendor’s site.)
Give it real context instead of expecting it to guess
Whichever you use, supplying context up front beats round after round of follow-up. With Cursor, spell out the relevant files, the behavior you want, and the edge cases, so it changes the right place within your project. With GitHub Copilot, lean on function names, comments, and existing code to steer its completions. Better context still doesn’t change one fact: the output can contain logic or security faults, and the more fluent it looks, the more closely you should read what it actually changed.
Review the diff change by change, don’t accept whole blocks
Once the AI writes code, the first move is to read the diff, not to run it. Cursor‘s automated edits can introduce defects, which matters most when it changes several files at once, so check each one; it suits developers who will inspect each change, not those who drop the review. The same holds for GitHub Copilot: every proposed change goes through diff review and tests before you merge it, no matter how right a completion looks. When it pulls in third-party code, licensing and provenance questions remain your call, not the tool’s.
Check before you ship
Run through these before merging. Treat the AI as a drafter, not the gate that lets code through.
- You read the diff for every AI change and checked it for logic and security faults; output from both Cursor and Copilot can carry defects.
- Command execution, secrets, and production systems have their own safeguards and aren’t left for an automated flow to touch (Cursor’s stated limit).
- Relevant tests were run and passed, not just eyeballed as “looks right.”
- You confirmed licensing and provenance for any third-party code the AI pulled in (Copilot leaves this to a human).
