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06 articles
Practical techniques for using AI coding assistants on legacy and brownfield codebases — giving enough context, preventing over-modernization, and validating changes that can't break.
How to feed failing tests directly to your AI coding assistant and use TDD as a feedback loop that keeps AI-generated code correct from the first run.
How to prompt AI coding assistants to produce diverse, realistic test fixtures and seed data — not just placeholder values that pass but don't catch real bugs.
How to prompt AI coding assistants to produce pure functions, injectable dependencies, and explicit interfaces — so the generated code is easy to test before you write a single assertion.
How to prompt AI coding assistants to generate tests that actually catch bugs — edge cases, failure modes, and boundary conditions, not just happy-path boilerplate.
A practical checklist for reviewing AI-written code — catching hallucinated APIs, subtle logic bugs, and security gaps before they reach production.