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