RAG in one paragraph
RAG is an architectural pattern, not a model feature. The flow is: a user asks a question, your system retrieves relevant chunks from a knowledge base (documents, wiki, database), the retrieved chunks are passed to the LLM as context alongside the question, and the LLM produces an answer grounded in the retrieved material. RAG solves the problem that LLMs do not natively know your private data and frequently hallucinate when asked about it.
Why RAG matters
Three operational problems RAG solves:
- Private data access — your customer records, internal policies, product documentation are not in the LLM's training data. RAG retrieves them at query time.
- Hallucination control — LLMs without grounding will produce plausible-sounding but incorrect answers when asked about specifics. RAG lets the LLM cite the source, dramatically reducing hallucination.
- Data freshness — LLMs are trained on a snapshot of internet text; they do not know about today's product release or last week's policy update. RAG operates on whatever is in your retrieval index, including newly added content.
RAG architecture components
Production RAG involves more than "call an embedding API and put the result in the prompt." The components are:
- Document ingestion pipeline: Read source documents, parse, structure-aware chunking, metadata extraction
- Embedding generation: Each chunk gets an embedding vector via an embedding model (OpenAI text-embedding-3, Cohere embed-v3, Voyage, open-source alternatives)
- Vector store: pgvector, Pinecone, Weaviate, Qdrant, Chroma — chosen on corpus size, query rate, operational posture
- Retrieval logic: Dense vector search, often hybrid with BM25 keyword search, frequently re-ranked by a cross-encoder
- Generation prompt: The retrieved chunks are formatted into a prompt template that instructs the LLM to answer using only the provided context
- Citation discipline: The LLM's response cites the chunks it used; hallucination-detection compares answer claims to retrieved context
- Eval harness: Golden-question retrieval evals, judge-LLM scoring, drift monitoring as a CI signal
Where RAG fits in enterprise Claude deployments
Most enterprise Claude deployments use RAG. The corpus shapes vary — legal contract repositories, medical guidelines, engineering documentation, customer-service knowledge bases, regulatory filings — but the pattern is consistent. NINtec's RAG practice has shipped systems across these corpus types with production-grade chunking, citation discipline, and eval-bar enforcement.