Retrieval-Augmented Generation (RAG)
A pattern that grounds LLM answers in your private documents by retrieving relevant snippets at query time.
RAG combines an LLM with a search system. When a user asks a question, the system first retrieves relevant chunks from a vector database, then passes those chunks to the LLM as context. The model answers using both its trained knowledge and the retrieved evidence.
This is the most common way enterprises deploy LLMs on proprietary data — policy manuals, contracts, support tickets, product catalogs — without expensive fine-tuning and without leaking data into a model's weights.
A production RAG stack has four moving parts: an embedding model, a vector store (Pinecone, Weaviate, pgvector), a retriever (often hybrid keyword + semantic), and an LLM for synthesis.
Key points
- No model retraining required — you update the index instead
- Citations can be surfaced back to the user for trust and auditability
- Chunking strategy (size, overlap, metadata) drives answer quality more than model choice
- Hybrid search (BM25 + vectors) beats pure semantic search in most enterprise cases
Common use cases
Frequently asked
Related terms
A neural network trained on massive text corpora to understand and generate human-like language.
A database optimized for storing and searching high-dimensional embeddings by similarity.
A numerical vector that captures the semantic meaning of a piece of content.
Continuing training of a pre-trained model on domain-specific data to specialize its behavior.
Applying Retrieval-Augmented Generation (RAG) in your business?
We help enterprises design, ship, and govern AI systems end-to-end.