Chapter 30: Key Takeaways

Code Review and Quality Assurance — Summary Card

AI-generated code demands more rigorous review, not less. Developers who commit AI-generated code take full responsibility for its correctness, security, and maintainability. The speed of AI code generation must not outpace the thoroughness of review.
Use AI as a first-pass code reviewer with structured prompts. Standardized review prompts targeting specific quality dimensions (correctness, security, maintainability, conventions) yield far better results than generic "review this code" requests. AI review supplements but never replaces human review.
Implement quality gates progressively. Start with formatting and basic linting (Phase 1), add type checking and security scanning (Phase 2), then introduce complexity thresholds and strict coverage minimums (Phase 3). Trying to enforce everything at once leads to team frustration and workarounds.
Ruff is the modern standard for Python linting. At 10-100x faster than alternatives, Ruff replaces flake8, isort, pyupgrade, and many other tools in a single package. Combine it with mypy for type checking and Bandit for security scanning to cover the critical analysis dimensions.
Track both cyclomatic and cognitive complexity. Cyclomatic complexity measures testability (number of independent paths), while cognitive complexity measures human readability (accounting for nesting depth and non-linear flow). Set explicit thresholds: cyclomatic complexity under 10, cognitive complexity under 15 per function.
Technical debt in AI-generated code follows predictable patterns. Watch for pattern repetition (duplicated similar code), outdated patterns (deprecated APIs), missing abstractions, superficial error handling, and configuration drift. Tag debt explicitly with structured comments for systematic tracking.
Effective peer review is constructive, scoped, and time-boxed. Critique code, not coders. Use feedback labels ([MUST], [SHOULD], [COULD], [NIT], [QUESTION]). Keep reviews to 200-400 lines, time-boxed to 60-90 minutes. Review within 24 hours of submission.
Review checklists prevent oversight. Maintain checklists covering correctness, security, performance, maintainability, testing, and AI-specific checks (hallucinated APIs, assumption validation, convention alignment). Include AI assistance disclosure in PR templates.
Continuous quality monitoring makes quality visible and actionable. Track metrics across code health, test health, process health, and dependency health. Display trends prominently—trends matter more than absolute values. Set alert thresholds for quality degradation.
The Quality Ratchet prevents gradual degradation. Establish a rule that key quality metrics can only improve, never decline. Enforce this in CI so that no merge can reduce test coverage below the current minimum or increase average complexity above the current maximum.
Quality culture depends on leadership, visibility, and shared ownership. Leaders must model quality practices. Quality metrics must be visible to the entire team. Quality is everyone's responsibility, not a separate team's job. Invest 20% of development time in quality improvement.
Balance speed and quality by thinking long-term. Cutting quality corners accelerates initial development but creates compounding costs through technical debt, production incidents, and maintenance burden. The perceived tension between speed and quality is largely a false dichotomy.
Guard against false confidence in automated tools. AI review, linters, and type checkers catch many issues but miss business logic errors, architectural problems, and organizational context. Automated tools are a safety net, not a guarantee.
Make quality a conversation, not a gate. The most effective quality programs foster ongoing discussion about quality standards, review practices, and improvement opportunities. Quality gates enforce minimums; quality culture drives excellence.