Get the Most Out of AI Pair Programmers (Copilot, Cursor, Claude Code)
Team-level practices for AI-assisted coding that raise velocity without rotting your codebase — context, prompting, review, and measurement.
- Give the AI context on purpose: point it at your style guide, architecture docs, and test conventions. Tools like Cursor and Claude Code index your repo — the difference between mediocre and great output is usually context quality, not model quality.
- Prompt for the plan, not just the code: ask the tool to outline its approach first for non-trivial changes. Catching a bad design in a five-line plan beats untangling a fifty-line diff.
- Generate tests alongside implementation — ask for the test first, then the code to pass it. AI writes boilerplate tests happily, and they're what let you refactor AI-generated code with confidence later.
- Review AI code harder than human code, not less: it fails in confident, plausible ways (subtle off-by-ones, swallowed exceptions, outdated API usage). Enforce the same CI gates, and watch for license and secrets hygiene in generated snippets.
- Measure what matters: track cycle time, rework rate, and defect escape rate per team. If velocity jumps but rework doubles, you've moved the bottleneck, not removed it — adjust prompting and review practices accordingly.