Ai Engineering Playbook

AI-Assisted Engineering Playbook

A practical playbook for engineering teams actively adopting AI coding tools (Copilot, Cursor, Claude, etc.) in product engineering. This document outlines how to structure your codebase, manage knowledge, define rules, manage context, and enforce testing to ensure AI accelerates delivery without degrading architecture, security, or quality.

**Core Principle:** AI-generated code is never "done" just because it compiles. It is only done when deterministic tools, tests, and human review confirm its behavior. AI accelerates typing and scaffolding, but it does not replace engineering discipline.

Contents

1. Core Principles
2. Make the Codebase AI-Readable
3. Knowledge Base Infrastructure
4. Context and Data Boundaries
5. AI Tooling Security
6. Rules Architecture
7. Granular Tool & Context Profiles
8. Meta Repository Pattern (Microservices)
9. The AI SDLC (Software Development Life Cycle)
10. Role Separation
11. The Context Delegate Pattern
12. Parallel & Background Agents
13. AI Change Budgets
14. Testing and Verification Ladder
15. Mandatory CI/CD Gates
16. AI Code Review Checklist
17. Debugging and Escalation Rules
18. Common Failure Modes (Field Catalog)
19. Model Benchmarking and Selection
20. Example Rules Library
21. [Example Operating Manual (Prom