RAG — retrieval-augmented generation — is the dominant production pattern for LLM applications that need to answer questions from a specific document corpus (internal knowledge bases, product docs, legal cases, customer support tickets).
How it works: (1) documents are chunked and converted to vector embeddings stored in a vector database; (2) at query time, the user's question is embedded, and the most similar chunks are retrieved; (3) those chunks plus the question are passed as context to an LLM, which generates an answer grounded in the retrieved text.
RAG dramatically reduces hallucination because the model answers from source material rather than parametric memory. It is what powers chatbot Q&A, semantic search over private data, and enterprise assistants. Nagro Solutions builds RAG systems using OpenAI, Anthropic, Pinecone, Weaviate, pgvector, and custom orchestration in Node or Python.