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AI Native Product Engineering — The TextbookCompanion to AIN 401 →
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  1. ·About This Book & Setup
  2. 1The AI-Native SDLC: From Agile to AI-DLC
  3. 2How Foundation Models Work: Tokens, Context, Sampling and Cost
  4. 3Prompt & Context Engineering; Structured Outputs
  5. 4Embeddings, Vector Search & the RAG Pipeline
  6. 5Production RAG: Chunking, Hybrid Search, Reranking & Citations
  7. 6AI-DLC Inception: Mob Elaboration & the Capstone Proposal
  8. 7Agents & Tools: Function Calling, MCP, Memory, Budgets and Guardrails
  9. 8Multi-Agent Patterns & Orchestration
  10. 9Evaluation Engineering: Golden Sets, LLM-as-Judge & Regression Evals in CI
  11. 10Securing AI Systems: OWASP 2026, Prompt Injection, Guardrails & Red-Teaming
  12. 11AI-DLC Construction: AI Code Review, Testing, Supply Chain & Traceability
  13. 12LLMOps: Observability, SLOs, Cost Control & Safe Deployment
  14. 13Responsible AI & Compliance: NIST AI RMF, EU AI Act, ISO/IEC 42001 & System Cards
  15. 14Model Strategy & Product Metrics: Prompting vs RAG vs Fine-Tuning
  16. 15Capstone Guide: Ship It, Prove It, Defend It
  17. 16Glossary & Re-Verification Checklist

Chapter 16 · updated 3 Oct 2026

Glossary & Re-Verification Checklist

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