Capstone Agentic AI System

The chapters in this module, in reading order.

#	Chapter
00	Capstone Project — The Five-Year-Old Version
01	Opening Failure — Parts Pass, System Fails
02	System Design Blueprint — Start With the User, Not the Model
03	Architecture Choices — Single Pipeline vs RAG vs Agent vs Multi-Agent
04	Data Pipeline Design — Retrieval, Context Assembly, Freshness, Chunking
05	Implementation Strategy — Build Order, Vertical Slices, Iteration Discipline
06	Prompt Engineering for the Project — System Prompts, Few-Shot, Chain-of-Thought
07	Evaluation Design — End-to-End Evals, Not Just Component Checks
08	Monitoring and Observability — Traces, Dashboards, Alerting on Quality Drift
09	Cost and Latency Management — Token Budgets, Caching, Model Routing
10	Deployment Strategy — Staging, Canary, Rollback, CI/CD for AI Systems
11	Presentation and Portfolio — Demo, Writeup, What Interviewers Actually Want
12	Integration Debugging — When the Whole System Breaks
13	Honest Admission — What We Do Not Fully Understand About Building AI Systems