RAG Pipelines Explained: Making AI Work with Your Own Data

What You'll Achieve

By the end of this course, you'll build a working RAG pipeline that connects any LLM to your own documents — no fine-tuning, no API gymnastics, no PhD required.

Understand RAG architecture — how retrieval, chunking, embedding, and generation work together as a system

Choose the right vector database — Pinecone, Chroma, Weaviate, or pgvector for your scale and budget

Optimize chunking strategies — semantic chunking, recursive splitting, and metadata tagging that actually improve retrieval quality

Deploy to production — handle real users, real documents, and real latency requirements with guardrails

Course Modules

Five modules, sequential. Each builds on the last. Start from Module 1 — no skipping.

Module 1 of 5

What RAG Actually Is (And Why You Need It)

28 min · 3 lessons

Why LLMs hallucinate and how retrieval grounding fixes it — with real before/after output comparisons
The 4-component RAG architecture: Document Loader → Chunker → Embedder → Retriever → Generator
When RAG beats fine-tuning: cost analysis showing $50/month RAG vs. $2,000+ fine-tuning for most use cases

We'll build a simple RAG demo using LangChain and OpenAI in under 30 lines of code — you'll see retrieval working in real-time.

Chunking & Embedding: The Foundation That Makes or Breaks RAG

35 min · 4 lessons

Fixed-size vs. semantic vs. recursive chunking — benchmark results showing 40% retrieval improvement with recursive splitting
Choosing embedding models: OpenAI text-embedding-3-small vs. open-source alternatives (BGE, E5) on MTEB leaderboard
Metadata enrichment: adding source, date, and category tags to chunks for filtered retrieval
Overlap strategies and why 15-20% overlap is the sweet spot for most document types

Hands-on: we'll chunk a 50-page PDF three different ways and compare retrieval accuracy side-by-side.

Unlock Modules 3–5

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Vector Databases: Storing and Retrieving at Scale

32 min · 3 lessons

Pinecone vs. Chroma vs. Weaviate vs. pgvector: managed vs. self-hosted, pricing at 10K vs. 1M vectors
Index types explained: HNSW, IVF, and flat indexes — when each matters for your latency vs. accuracy tradeoff
Hybrid search: combining dense vector similarity with sparse keyword matching (BM25) for 60% better recall

We'll spin up a Chroma instance locally, ingest 1,000 chunks, and run semantic queries in under 50ms.

Production RAG: From Prototype to Real Users

38 min · 4 lessons

Guardrails and validation: preventing prompt injection, filtering low-confidence retrievals, and citation tracking
Re-ranking retrieved results with cross-encoder models for 2x relevance improvement
Monitoring and observability: tracking retrieval quality, latency percentiles, and hallucination rates in production
Incremental indexing: updating your vector store when documents change without full re-embedding

Deploy a RAG API with FastAPI that handles 100 concurrent requests with sub-2-second response times.

Advanced RAG: Multi-Modal, Agents, and Beyond

30 min · 3 lessons

Multi-modal RAG: retrieving images, tables, and charts alongside text using CLIP and multimodal embeddings
Agentic RAG: letting the LLM decide when to retrieve, what to retrieve, and when to answer from its own knowledge
Self-RAG and corrective RAG: architectures that evaluate their own retrieval quality and retry when confidence is low

We'll build an agentic RAG system that routes questions to different knowledge bases based on topic classification.