RAG

RAG (Retrieval-Augmented Generation) pairs an LLM with a search engine over your documents. Before answering, the system retrieves the relevant passages and provides them to the model.

This approach grounds responses in real sources, which reduces hallucinations and lets the AI draw on up-to-date, private data.

RAG relies on embeddings and a vector database to retrieve information by meaning rather than by keywords.