What is RAG (Retrieval-Augmented Generation)? Explained simply

What RAG is

Retrieval-Augmented Generation (RAG) is a pattern where, instead of relying only on what a model learned during training, you retrieve relevant documents at query time and feed them to the model as context. The model then answers grounded in your data — reducing hallucinations and keeping answers current.

The pipeline, step by step

1. Chunk your documents into passages.
2. Embed each chunk into a vector with an embedding model.
3. Store the vectors in a vector database.
4. At query time, embed the question, find the nearest chunks, and pass them to the LLM as context.

The free building blocks

You can assemble a complete, zero-cost RAG stack from open-source parts:

• An embedding model (multilingual options exist).
• A vector database for similarity search.
• A pipeline to tie chunking, retrieval, and generation together.
• An eval harness to measure answer quality.

All four kinds are on ai-supply, each one security-scanned. Browse the data and NLP categories to assemble your stack, and compare options on the leaderboards.

RAG is the fastest way to make an LLM useful on your own content. Start from a vetted pipeline instead of from scratch — explore the catalog.