Course duration

• 2 days

Course scope

A framework for reviewing AI-generated code for security, correctness, and long-term maintainability from common AI failure patterns, a PR review checklist, CI quality gates (static analysis, security scanning, coverage thresholds), and team rollout of an AI code review policy. Stack-agnostic; concepts apply to any language or framework.

Who is it for

Engineering teams already using AI coding assistants in daily work, and the tech leads, staff engineers, or engineering managers responsible for code quality and incident rates and not a beginner "how to use Copilot" session.

Course description

1. The AI code risk landscape: what the 2026 data actually shows
2. Reading AI-generated code like a reviewer, not an approver
3. Common AI failure patterns: hallucinated APIs, duplicated logic, silently wrong edge cases
4. Comprehension debt: spotting code your team can't safely evolve
5. Security review checklist for AI-generated code
6. Hands-on: reviewing real AI-generated pull requests
7. Designing a PR review checklist for AI-assisted work
8. Automated quality gates: static analysis, security scanning, coverage thresholds in CI
9. Deciding when AI output needs a human-written explanation
10. Metrics that matter: incident rate and revert rate vs. lines generated
11. Rolling out an AI code review policy to your team
12. Case study, team workflow design workshop, and Q&A