RAG connects an LLM to your organisation's documents via embeddings and vector search. Answers cite retrieved chunks, improving accuracy for internal knowledge bases.
A typical architecture includes ingestion pipelines, chunking strategy, embedding model selection, vector store (Pinecone, pgvector, Azure AI Search), and evaluation harnesses.
UK regulated industries favour RAG over fine-tuning when data freshness and auditability matter. Security teams require access controls on indexed content.