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.
Written by the RadarTrek editorial team · Reviewed June 2026
About this course
Retrieval Augmented Generation (RAG) is the technique that lets you ground an AI model's responses in your own documents, database, or knowledge base — preventing hallucination and enabling factually accurate answers about your specific content. Learning RAG means understanding vector embeddings, similarity search, vector databases, and how to combine retrieval with generation in a reliable pipeline. This RAG tutorial covers the complete workflow from document ingestion to production-ready question-answering over your own data.
RAG is the architecture behind most enterprise AI applications: internal knowledge base chatbots, customer support systems, legal research tools, and code-aware assistants. After completing this course you will be able to ingest documents into a vector database, perform semantic similarity search, and build a RAG pipeline that answers questions accurately from your own content — using Supabase pgvector and the Claude or OpenAI API.
Frequently asked questions
What is a vector embedding and why do RAG systems need them?
A vector embedding is a mathematical representation of text as a list of numbers that captures semantic meaning — words and sentences with similar meanings produce similar vectors. RAG systems use embeddings to find documents semantically relevant to a query, even if they do not share exact keywords. You embed both documents and the query, then find documents whose vectors are closest to the query vector — that is semantic search.
What vector database does this course use?
This course uses Supabase pgvector, a PostgreSQL extension that adds vector storage and similarity search. Pgvector is an excellent production choice because it runs inside your existing PostgreSQL database, requires no additional infrastructure, and supports hybrid search combining vector similarity with traditional SQL filters. Alternatives like Pinecone, Weaviate, and Qdrant are also discussed.
What types of documents can I use with RAG?
RAG works with any text-based content: PDFs, Word documents, Markdown files, web pages, database records, code files, and support documentation. The ingestion pipeline extracts text, chunks it into segments, generates embeddings for each chunk, and stores them with metadata. This course covers PDF and Markdown ingestion, with patterns that extend to other content types.
How is RAG different from just putting all my documents in the prompt?
Context windows limit how much text you can include in a single prompt — even large-context models have limits, and larger prompts cost significantly more to process. RAG retrieves only the most relevant 3–5 document chunks for each query. This makes responses both cheaper and more accurate, because a focused excerpt is easier for the model to reason about than an overwhelming mass of potentially irrelevant text.
What is chunking and how should I split my documents?
Chunking splits documents into smaller segments before embedding. Chunk size is a key parameter — chunks that are too small lose context, chunks that are too large become unfocused and expensive. Most RAG systems use chunks of 500–1000 tokens with 100–200 token overlaps between adjacent chunks to preserve continuity. This course covers chunking strategies and explains how to tune them for different content types.