RAG and Vector Search
Retrieval-Augmented Generation is the most practical AI pattern for builders: give a language model access to your own documents and it answers questions accurately, without hallucinating. This course takes you from the theory of vector embeddings through to a production RAG pipeline you can actually ship — with Supabase pgvector, Voyage AI, and Claude.
What you'll learn
Course outline
Free — no account needed
What RAG Is — and Why Fine-Tuning Is Usually the Wrong Answer
The mental model that clarifies when to use RAG, fine-tuning, or neither
Vector Embeddings — What They Are and How to Choose a Model
The mathematics-free explanation of why semantic search works and which embedding model to use
Supabase pgvector — Your First Semantic Search
Set up the vector database, store embeddings, and run your first similarity query
Full course — $79 one-time
Chunking Strategies — How to Split Documents for Retrieval
The chunking decisions that most affect retrieval quality — and how to get them right
Measuring and Improving Retrieval Quality
How to know if your RAG pipeline is actually finding the right content — and how to fix it when it is not
The Full RAG Pipeline — Ingest, Retrieve, Augment, Generate
Wire everything together into a production-ready pipeline with proper system prompt design
Production RAG — Caching, Latency, and Cost
Make your RAG pipeline fast and affordable at scale
Evaluating Your RAG System with LLM-as-Judge
Build an automated evaluation pipeline that tells you whether your answers are actually correct
Get the full course
8 lessons — from embeddings to production RAG with quality evaluation.