RAG From Zero to Production: A Complete Beginner Case Study

Most RAG tutorials stop at "it answered one question." This one builds a complete RAG system as a case study — Kube Help, an assistant that answers Kubernetes questions from the official docs — from scoping the problem to a deployed, monitored application. At every step you'll see the decisions a developer makes, what to watch out for, and a short code snippet you can reuse. Every tool used has a free or local option. Chapters 0:00 Intro: what we're building 0:51 Step 1 — Scope: problem, users, success criteria 3:36 Step 2 — Data selection (license, freshness, sensitive data, formats) 6:20 Step 3 — Parsing and chunking 8:26 Step 4 — Embeddings and the index (Postgres + pgvector) 10:53 Step 5 — Retrieval: hybrid search, RRF and re-ranking 13:22 Step 6 — Generation: prompt, citations, refusals 15:47 Step 7 — Wrapping it in an API (FastAPI) 17:17 Step 8 — Evaluation: retrieval metrics, LLM judge, human review, CI 21:44 Step 9 — Deployment: Docker, where to deploy, CI/CD, cost, security 26:02 Step 10 — Monitoring and maintenance 28:09 Recap: the end-to-end checklist Stack shown: Python · python-frontmatter · sentence-transformers (BAAI/bge-small-en-v1.5, BAAI/bge-reranker-base) · Postgres + pgvector · OpenAI-compatible LLM client (or Ollama locally) · FastAPI · pytest · Docker / Docker Compose Data: Kubernetes documentation (kubernetes/website), licensed CC BY 4.0 Note: the experiment-log numbers in the evaluation chapter are illustrative. Series: RAG Query Processing · RAG Evaluation · Advanced RAG Patterns · this case study What data will you build your RAG on? The top answer becomes the next case study. #RAG #LLM #AIEngineering Tags RAG tutorial, build RAG from scratch, RAG end to end, RAG case study, retrieval augmented generation, RAG project, RAG deployment, RAG evaluation, pgvector, Postgres vector search, hybrid search, reranking, cross encoder, FastAPI RAG, Docker RAG, LLM app deployment, RAG for beginners, production RAG, sentence transformers, Ollama RAG, LLM evaluation, AI engineering