RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is a way of making an AI model answer from your own documents: the system first finds the most relevant passages, then gives them to the model with the question, and the model answers using only that material, ideally citing where each point came from.
Key Facts
| Typical sources | Manuals, policies, product catalogues, past proposals, SOPs, support tickets |
|---|---|
| Main benefit | Answers grounded in your material, updated as soon as documents change |
| Accuracy levers | Clean documents, good chunking, relevance ranking, citation checks |
| Typical pilot | 4–8 weeks on one document set and one user group |
How RAG works
- Documents are split into passages and indexed for search.
- A question retrieves the most relevant passages.
- The model answers from those passages and cites them.
RAG or fine-tuning
Use RAG when answers must reflect current documents and be traceable. Fine-tuning changes how a model writes or classifies, not what it knows today. See RAG or fine-tuning.
Where it fails
Poor or contradictory source documents, scanned files without text recognition, and questions the documents do not answer.
Frequently Asked Questions
Does RAG stop hallucination completely?
It reduces it a lot, especially with citations and instructions to say "I don't know", but outputs should still be checked for high-stakes uses.
Can RAG work with Hindi documents?
Yes, with models and search that handle Hindi well. Test on your real documents before committing.
Related Glossary
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