Give an LLM access to your own documents and data so it can answer using information outside its built-in knowledge.
Retrieval-Augmented Generation, or RAG, combines information retrieval with language generation. Instead of asking a model to answer entirely from what it already knows, the application first finds relevant information from a knowledge source and then gives that information to the model as context.
A typical RAG system loads and splits documents, creates embeddings, stores them in a vector database, retrieves relevant chunks for a question, and passes those chunks to the model. The quality of the final answer depends heavily on retrieval quality, chunking, metadata, context construction, and the model itself.
What you'll walk away knowing