Ship Code You Trust.
Every Single Time.

Yes, but only with structured context. Modern AI tools like GitHub Copilot code review (GA since April 2025, used by over 1 million developers in its first month) can detect common vulnerabilities such as SQL injection, XSS, and insecure deserialization. The key is providing the right context: the function under review, its callers, and any auth middleware. We teach a "Security Lens" prompting technique that systematically checks OWASP Top 10 categories against your code changes.

Coverage theater happens when tests assert trivial behavior like "function returns something" instead of verifying actual business logic. We teach a Discovery-Verification-Implementation loop: first use AI to enumerate edge cases and failure modes, then verify each scenario maps to real business requirements, and only then generate test implementations. This produces tests that catch regressions instead of just inflating coverage numbers.

AI code review excels at catching pattern-based issues instantly: style violations, common bugs, missing null checks, and security anti-patterns. Human review excels at architectural judgment, business logic validation, and identifying subtle design problems. The senior workflow uses both: AI handles the first pass to catch mechanical issues, freeing human reviewers to focus on design, maintainability, and whether the code actually solves the right problem. Studies show developers using AI review spend 3x longer in review but ship 60% more features.

Yes. The mental models are framework-agnostic. Whether you use Vitest, Jest, PHPUnit, Pytest, or Go testing, the principles of AI-assisted review and testing remain identical. The course demonstrates with Vitest and Playwright for frontend, and PHPUnit for backend, but every technique transfers directly to your stack because the core skill is structuring prompts and context, not framework syntax.

Hallucinations in review typically manifest as the AI flagging non-existent issues or suggesting APIs that do not exist. We teach a "Ground Truth" method: always include the relevant type definitions, interface contracts, and dependency versions in your review context. When the AI can see the actual types and signatures, hallucination rates drop dramatically. We also teach you to recognize the telltale signs of a hallucinated review comment so you can discard it immediately.

This is one of the highest-value applications of AI in testing. Legacy code is hard to test because understanding what it does requires archaeological effort. AI can read a function, infer its contract from usage patterns, and generate a characterization test suite that captures current behavior. You then use these tests as a safety net while refactoring. The course covers a specific "Legacy Wrap" technique where you feed AI the function, its callers, and its database queries, and it produces tests that document the actual behavior before you change anything.

This is a deep-dive into specific chapters of Build Fast With AI. You get full access to all 12 chapters with one purchase, including the code review framework (Chapter 6), testing strategies (Chapter 8), and debugging workflows (Chapter 9). All future updates are included.