What is RAG? Retrieval-Augmented Generation, explained

Why RAG exists

An LLM only knows what it saw in training — it can't see your private documents, and its knowledge has a cutoff date. Ask it about your data and it will confidently make something up. RAG fixes that by fetching the right information at query time and putting it in front of the model, so the answer comes from your facts, not the model's memory.

• Grounds answers in your documents, not the model's training data
• Keeps knowledge current without retraining the model
• Lets answers cite the exact source, so they're verifiable

How the RAG pipeline works

A RAG system runs a short sequence on every question. Your documents are converted to clean text and split into passages; each is indexed by meaning (embeddings) and often by keyword; a question retrieves the closest passages, the best implementations rerank them, and the model writes an answer using only that retrieved context — with citations.

• Ingest: documents → clean, chunked text
• Index: store passages for semantic (and keyword) search
• Retrieve: pull the most relevant passages for the question
• Rerank: order them by true relevance (a cross-encoder beats vector-only)
• Generate: answer from the retrieved passages, and cite them

RAG vs fine-tuning

Fine-tuning changes how a model writes; RAG changes what it knows. For facts that update — docs, policies, product data — RAG is cheaper, faster to update (just add a document), and traceable (it cites sources). Fine-tuning is better for teaching style or format, not for keeping a model current on your knowledge.

• Use RAG for factual, changing knowledge you need cited
• Use fine-tuning for tone, format, or task behavior
• RAG updates instantly — add or remove a document, no retraining

Skip building RAG — use Kit for AI

A production RAG stack means running a vector database, an embedding pipeline, a chunking strategy, a reranker, and the glue between them. Kit for AI is that pipeline, managed: upload documents or URLs, and query them from the app, REST API, or MCP. Retrieval is hybrid (vector + keyword) with reranking, answers cite their sources, and everything runs on private local models.

• No vector database, embedding pipeline, or chunking to build
• Hybrid search + reranking out of the box — higher precision than vector-only
• Grounded, cited answers on private models; query over API and MCP

FAQ

What does RAG stand for?

RAG stands for Retrieval-Augmented Generation — a technique that retrieves relevant passages from your documents and gives them to an LLM as context, so answers are grounded in your data and can cite their source.

How is RAG different from fine-tuning?

Fine-tuning changes how a model writes; RAG changes what it knows. RAG is better for factual, changing knowledge because you just add a document — no retraining — and answers cite their source.

Do I need a vector database for RAG?

A traditional RAG build does. With Kit for AI you don't — retrieval, indexing, and reranking are managed, so there's no vector database, embedding pipeline, or chunking to run.

Does RAG stop hallucinations?

It sharply reduces them by grounding answers in retrieved passages. Kit for AI goes further: answers cite their source and decline when your documents don't cover the question.