RAG Fundamentals for Everyone
Master the core building blocks of RAG, from embeddings to agentic retrieval.
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RAG is the system that gives an LLM access to your documents. Done well, it's the difference between an assistant that makes things up and one that cites a specific paragraph of your internal wiki. Done badly, it's the reason your chatbot confidently answers questions using last year's price list.
Curated by Param Harrison
These courses cover the full retrieval surface: fundamentals (embeddings, vector stores, chunking strategies), production patterns (hybrid search, reranking, vectorless approaches), and evaluation (how you know your retrieval is actually working). Start at the level you need and work up to agentic RAG for complex queries.
Showing 15 of 15 courses
Common questions
Give the LLM a short-term memory. You turn your documents into embeddings, store them in a vector database, and at query time you fetch the chunks most relevant to the user’s question and hand them to the model. The model reads those chunks and answers grounded in them instead of hallucinating.
Usually yes for production. Small prototypes work fine with an in-memory list or SQLite + FAISS. Once you hit ~100k documents, a dedicated vector store (Qdrant, Pinecone, pgvector) pays for itself in latency and operational simplicity.
Vector similarity plus keyword search, combined with a reranker. It beats pure vector search on tasks where exact tokens matter (codebases, legal text, product SKUs). The Hybrid Search Qdrant course covers the full implementation.
Yes. The Vectorless RAG course covers hierarchical document trees and summarization chains that replace the vector store entirely for certain document shapes. It’s niche but powerful when it fits.
Evaluation. Build a fixed question + expected-context dataset and measure recall at k. The LLM Observability course and the Advanced RAG Evaluation course both cover the eval setup.