ModelRefs / RAG (Retrieval-Augmented Generation) — AI Glossary
RAG (Retrieval-Augmented Generation) — AI Glossary
An architecture that retrieves relevant documents from an external store and injects them into the LLM prompt at inference time.
Overview
RAG separates knowledge (in a vector or keyword index) from reasoning (in the LLM). It enables citation, freshness, and per-tenant data without retraining. Typical pipeline: chunk → embed → store → retrieve → rerank → generate.
Reference details
| Topic | rag |
|---|---|
| Also known as | Retrieval Augmented Generation, retrieval-augmented generation |
| Last reviewed | 2026-06-24 |
Related terms
Example: Grounding an answer that changes weekly
“What is our current refund window?” Asked cold, the model answers from training data — a plausible 30 days that may be wrong. With RAG, the policy document is retrieved and injected, so the answer reflects the version in the index and can cite it. Change the policy, reindex, and the answer changes with no retraining.
Commonly confused with
RAG is not a fix for hallucination — it changes what the model hallucinates about. If retrieval returns nothing relevant, a model that has not been told to abstain will still answer, now with misplaced confidence because context was present. Groundedness has to be measured, not assumed.
When to use it
Reach for it when:
- The knowledge changes faster than you would retrain
- Answers must cite a source
- Data is per-tenant or access-controlled and cannot go into weights
Reach for something else when:
- You need a change in behaviour, tone or format — that is fine-tuning
- The whole corpus fits comfortably in context and stays small
- Retrieval quality is poor: bad context makes answers worse than no context
Referenced by
This term is used by the following ModelRefs references:
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Frequently asked questions
What is RAG (Retrieval-Augmented Generation)?
An architecture that retrieves relevant documents from an external store and injects them into the LLM prompt at inference time.
Is RAG (Retrieval-Augmented Generation) the same as Retrieval Augmented Generation?
Yes — Retrieval Augmented Generation, retrieval-augmented generation are common aliases for RAG (Retrieval-Augmented Generation).
What concepts are related to RAG (Retrieval-Augmented Generation)?
Closely related concepts include embedding, vector database, semantic search, grounding.