Textbook
AI Native Product Engineering — The Textbook
The companion textbook for AIN 401: sixteen chapters, each with motivation, history, core concepts, a worked example, pitfalls, review questions, tools and key takeaways. Open-source-first and laptop-friendly.
—About This Book & Setup1The AI-Native SDLC: From Agile to AI-DLCWeek 12How Foundation Models Work: Tokens, Context, Sampling and CostWeek 23Prompt & Context Engineering; Structured OutputsWeek 34Embeddings, Vector Search & the RAG PipelineWeek 45Production RAG: Chunking, Hybrid Search, Reranking & CitationsWeek 56AI-DLC Inception: Mob Elaboration & the Capstone ProposalWeek 67Agents & Tools: Function Calling, MCP, Memory, Budgets and GuardrailsWeek 78Multi-Agent Patterns & OrchestrationWeek 89Evaluation Engineering: Golden Sets, LLM-as-Judge & Regression Evals in CIWeek 910Securing AI Systems: OWASP 2026, Prompt Injection, Guardrails & Red-TeamingWeek 1011AI-DLC Construction: AI Code Review, Testing, Supply Chain & TraceabilityWeek 1112LLMOps: Observability, SLOs, Cost Control & Safe DeploymentWeek 1213Responsible AI & Compliance: NIST AI RMF, EU AI Act, ISO/IEC 42001 & System CardsWeek 1314Model Strategy & Product Metrics: Prompting vs RAG vs Fine-TuningWeek 1415Capstone Guide: Ship It, Prove It, Defend ItWeek 1516Glossary & Re-Verification Checklist