Claude Code Best Practices for Production Applications

I have used Claude Code on over a dozen production applications at this point. Some of those projects went smoothly from day one. Others were a mess until I figured out what I was doing wrong.

The difference was never Claude Code's capability. It was how I set up the project for it.

Here is everything I have learned about making Claude Code work reliably in production.

CLAUDE.md Is Your Project's Brain

The single most impactful thing you can do is write a thorough CLAUDE.md file. Think of it as your project's operating manual for AI agents. This is not optional documentation. This is the difference between an agent that guesses and an agent that knows.

Your CLAUDE.md should include:

Architecture decisions. Why you chose Convex over Supabase. Why you use server components by default. Why your API layer follows a specific pattern. When the agent understands the "why," it makes better "how" decisions.

Coding conventions. Naming patterns, file organization rules, import ordering, error handling standards. Be specific. "Use camelCase for variables" is good. "Name boolean variables with is/has/should prefix" is better.

Testing requirements. What coverage level you expect. Which testing libraries you use. How to run the test suite. Whether you want integration tests for every API route or just the critical ones.

Deployment procedures. Build commands, environment variable requirements, deployment targets, rollback procedures. The agent should be able to deploy without asking you a single question.

The more context you provide, the more autonomous and accurate the agent becomes. I have seen CLAUDE.md files turn a mediocre AI experience into something that feels like working with a senior developer who has been on the project for months.

Project Structure Is Not Optional

Claude Code navigates well-organized codebases like a fish in water. It struggles with messy ones like everyone else does.

Rules I follow without exception:

Keep files under 500 lines. If a component file is approaching that limit, it needs to be split. This is not just for AI, it is good engineering. But the AI benefits disproportionately because it can hold an entire file in context without truncation.

Use consistent naming conventions. If your API routes follow a pattern, follow it everywhere. If your components use a specific directory structure, never deviate. Consistency lets the agent predict where things are and how they should look.

Clear separation of concerns. Business logic in one place. UI components in another. Database queries in their own layer. When responsibilities are cleanly separated, the agent modifies the right file every time instead of guessing.

Invest in clean architecture upfront. With traditional development, you can get away with some messiness early and clean it up later. With AI-assisted development, mess costs you immediately in the form of worse agent output. The ROI on clean architecture is dramatically higher when agents are involved.

Testing Is Where Claude Code Shines

I was skeptical about AI-generated tests. Then I saw the coverage reports.

Claude Code generates tests that I would not have thought to write. Edge cases with null inputs. Boundary conditions on pagination. Race conditions in async operations. The agent thinks about failure modes more systematically than I do because it does not get bored or impatient.

My setup: every new component gets unit tests, integration tests, and accessibility checks automatically. Not because I configured some special pipeline, but because my CLAUDE.md says "every component must have comprehensive tests" and the agent complies.

The safety net this creates is transformative. I iterate faster because I know the tests will catch regressions. I refactor with confidence because coverage is high. I deploy without anxiety because the automated gates catch problems before production.

Configure test generation alongside feature development, not after. When the agent writes the feature and the tests in the same session, the tests actually reflect the real behavior of the code. Tests written after the fact tend to test what the developer thinks the code does, not what it actually does.

Deployment Automation

The goal is a workflow where merging to main triggers automatic quality gates and production deployment. No manual steps. No "let me just check one thing" before deploying.

Claude Code integrates with CI/CD naturally. Your pipeline runs the build, the type checker, the test suite, and the linter. If everything passes, it deploys. If anything fails, it blocks and reports.

I set up automated builds, type checking, and deployment scripts that run without human intervention. The agent generates the CI/CD configuration as part of the project setup. GitHub Actions, Vercel deployment hooks, environment variable validation. All configured from day one.

The key insight is that deployment confidence comes from test coverage and type safety, not from manual review. If your tests are comprehensive and your types are strict, you can deploy every commit to production safely. Claude Code helps you get to that level of confidence faster than you could manually.

Common Mistakes I Made (So You Don't Have To)

Vague CLAUDE.md files. "Follow best practices" is useless. "Use Zod for all API input validation with detailed error messages" is useful. Be explicit.

Inconsistent project structure. I had one project where half the API routes used one pattern and half used another. The agent was confused constantly. I spent a day standardizing everything and the agent quality improved immediately.

Not reviewing early outputs carefully. The first few sessions with Claude Code on a new project, review everything line by line. Correct patterns early. Once the agent learns your preferences (through CLAUDE.md updates and consistent feedback), the quality stabilizes at a high level.

Skipping type definitions. TypeScript strict mode plus comprehensive type definitions is the single best investment for AI-assisted development. Types give the agent constraints that prevent entire categories of mistakes.

The Bottom Line

Claude Code is not magic. It is a tool that performs proportionally to how well you prepare it. A well-configured project with thorough documentation and clean architecture gets outstanding results. A messy project with no CLAUDE.md gets mediocre results.

Put in the setup work. The payoff is enormous.