The way to deliver fast with AI: Quality
Quality foundations—testing, strict types, clean APIs—enable rapid AI-assisted development because features emerge naturally once verification loops exist.
Most interactions with Claude Code focus on ensuring quality. Features just happen once architectural and testing foundations exist.
Key Insights
Mutation testing reveals blind spots. Within 20 minutes of setup, mutation testing exposed untested code paths that traditional coverage metrics missed.
Separate fast from slow tests. Unit tests run instantly; acceptance tests requiring Docker containers take 10-20 seconds each. Splitting them into separate workspaces maintains development speed without sacrificing thoroughness.
Clean APIs guide AI output. Fluent interfaces hide implementation details. Claude Code generates better documentation when the public API is clean and intention-revealing.
Flaky tests have distinct causes. Three patterns emerged: unbounded test concurrency, race conditions in health checks (solved via exponential backoff), and resource exhaustion from improper cleanup (fixed through automatic afterEach hooks).
The Core Trade-off
Automated verification loops require investment—mutation tests run slowly, specifications can become verbose—but they create a foundation where AI assistance reliably produces working code.
Connections
Complements claude-code-best-practices on structuring AI-assisted workflows around quality gates.