Best Practices for Claude Code in Production

Getting Claude Code to write code is easy. Getting it to write production-quality code that fits your team's standards, passes review, and doesn't introduce technical debt — that takes deliberate practice. These patterns come from teams running Claude Code daily on production codebases.

Give Claude Code context, not just instructions

The single biggest factor in output quality is context. Claude Code reads your codebase, but it doesn't know your team's unwritten rules unless you tell it.

  • Use CLAUDE.md — This file at the root of your project is Claude Code's first read. Put your architecture decisions, naming conventions, testing requirements, and deployment rules here.
  • Be explicit about patterns — "We use repository pattern for data access" is more useful than "follow best practices".
  • Reference existing code — "Follow the pattern in src/services/UserService.ts" gives Claude Code a concrete example to match.

Code review integration

AI-written code needs the same (or more rigorous) review as human-written code. Establish clear norms:

  • Always review diffs — Claude Code can create PRs. Every PR gets human review, same as any other contributor.
  • Use Claude Code as a reviewer too — Create a code review skill that checks for common issues before human review. This catches the obvious things so humans can focus on architecture and logic.
  • Track AI-authored commits — Use the Co-Authored-By trailer so your team knows which code was AI-assisted. This builds trust and makes auditing straightforward.

Quality gates

Don't trust any agent (human or AI) to self-certify quality. Build automated checks:

  • Tests must pass — Claude Code should run the test suite before committing. Configure this in your CLAUDE.md or as a pre-commit hook.
  • Linting and formatting — Enforce with pre-commit hooks. Claude Code respects these, and they catch style drift early.
  • Type checking — If you use TypeScript, tsc --noEmit should gate every commit. Claude Code writes TypeScript well, but type errors slip through without enforcement.
  • Build verification — If it doesn't build, it doesn't ship. Run npm run build as part of every task completion.

Task decomposition

Claude Code works best on well-defined, bounded tasks. Large, vague requests produce large, vague results.

  • One task, one session — "Add user authentication" is too broad. "Add JWT middleware to the Express app" is a good task. "Add login and signup pages using the JWT middleware" is the next task.
  • Define done — "The tests pass, the types check, and the feature works in the dev environment" gives Claude Code a clear target.
  • Sequence dependencies — If task B depends on task A, finish A first. Don't ask Claude Code to build on code that doesn't exist yet.

Iteration, not perfection

Claude Code rarely gets everything right on the first pass. The most productive teams treat the first output as a strong draft:

  • Review the output and provide specific feedback: "The error handling in processPayment should use our custom AppError class, not generic Error."
  • Let Claude Code iterate — it remembers the full context of your conversation.
  • After 2-3 iterations, if it's still not right, the task is probably under-specified. Add more context to CLAUDE.md or break the task down further.

Version control discipline

  • Branch per task — Claude Code should work on feature branches, never directly on main.
  • Small, focused commits — Encourage this in your CLAUDE.md. "Each commit should do one thing. Write clear commit messages explaining why, not what."
  • Never force-push — This should be in your CLAUDE.md guardrails. Claude Code respects explicit prohibitions.

Measuring impact

Track what matters:

  • Time to merge — How long from task start to PR merged? This captures the full cycle, including review.
  • Defect rate — Are AI-assisted PRs introducing more bugs? Track this against human-only PRs.
  • Cost per task — Token costs (or subscription costs) divided by tasks completed. Compare against developer time saved.
  • Rework rate — How often do AI-authored PRs need significant changes after review?

Next steps

Once your team has solid fundamentals, explore AgentOps for operating agents at scale, and Guardrails for more sophisticated safety controls.