Module 16: Production Capstone Project

Learning Objectives

• Build a complete enterprise RAG platform
• Integrate all components: ingestion, retrieval, generation, security, observability
• Deploy on Kubernetes with full production architecture
• Test with realistic enterprise scenarios

Why This Matters

This capstone proves you can architect and deploy a complete production AI system — not just chain API calls. It is the difference between "I built a chatbot" and "I engineered a production RAG platform." That distinction matters for career advancement.

Lesson Content

This is the capstone. You build a production-grade enterprise RAG platform that integrates everything from the previous 15 modules: document ingestion, chunking, embeddings, vector search, hybrid retrieval, reranking, AI agents, streaming, evaluation, observability, security, multi-tenancy, caching, and Kubernetes deployment.

What You Build

1. Document ingestion pipeline: PDF/Markdown/HTML parsing, semantic chunking, metadata enrichment
2. Vector search with Qdrant: HNSW index, metadata filtering, multi-tenant collections
3. Hybrid retrieval: BM25 + vector + RRF fusion + cross-encoder reranking
4. AI agents: Multi-tool agent with retrieval, database, and web search
5. Production API: FastAPI with streaming, auth, rate limiting, semantic caching
6. Evaluation: Retrieval metrics, hallucination detection, quality dashboards
7. Observability: OpenTelemetry tracing, token monitoring, cost tracking
8. Security: Prompt injection defense, tenant isolation, audit logging
9. Deployment: Docker + Kubernetes + CI/CD with quality gates

Technology Stack

Python, FastAPI, LangChain/LangGraph, Qdrant, Redis, Claude/OpenAI, sentence-transformers, cross-encoder, Docker, Kubernetes, OpenTelemetry, Prometheus, Grafana.

This Is Your Portfolio Piece

When you complete this capstone, you have a production-grade RAG system that demonstrates: scalable architecture, quality engineering, security awareness, operational maturity, and end-to-end engineering. This is what you discuss in interviews and present to engineering leadership.

Production Story

An enterprise team built their RAG system in 2 weeks. It took 3 months to make it production-ready: adding caching (cut costs 60%), implementing tenant isolation (required for enterprise customers), building evaluation (caught a 15% quality regression from a chunking change), and deploying observability (discovered a prompt injection attempt within the first week). The capstone teaches all of these lessons upfront.

Key Terms

Capstone

Final project integrating all course concepts into one production system

Quality Gate

CI/CD check blocking deployment if metrics degrade

Production RAG

RAG system with security, observability, multi-tenancy, and deployment automation

Hands-On Labs

1. Capstone: Production RAG Platform

   Build and deploy the full enterprise RAG platform.

   3 hours - Advanced

   • Build document ingestion pipeline
   • Deploy Qdrant with hybrid search and reranking
   • Build FastAPI API with streaming and caching
   • Add AI agents with tool calling
   • Implement evaluation and hallucination detection
   • Add OpenTelemetry observability
   • Implement prompt injection defense and tenant isolation
   • Deploy on Kubernetes with CI/CD quality gates
   • Run end-to-end tests with realistic enterprise queries
   • Document architecture decisions

   View lab files on GitHub

Key Takeaways

• Production RAG = ingestion + retrieval + generation + security + observability + deployment
• Every component from Modules 1-15 integrates into a cohesive platform
• Quality gates in CI/CD prevent regression on every change
• Security is not optional — prompt injection and data leakage are real threats
• This capstone is your proof of production RAG engineering competence