---
title: "Retrieval-augmented generation (RAG)"
type: "glossary-term"
category: "AI & LLMs"
canonical_url: "https://www.graphcanon.com/glossary/ai-and-llms/retrieval-augmented-generation"
updated: "2026-07-09"
---

# Retrieval-augmented generation (RAG)

_Also known as: RAG_

**RAG fetches relevant documents at query time and feeds them to a language model as context, so answers are grounded in your data instead of only the model's training.**

**Retrieval-augmented generation (RAG)** combines a retriever with a generator. When a question comes in, the system first retrieves relevant passages - usually via vector search over embeddings - then passes them to a language model as context so its answer is grounded in those sources.

RAG is the common alternative to fine-tuning when you need a model to answer over private, large, or frequently changing data without retraining it.

## In GraphCanon

Retrieval, vector database, and framework tools that implement RAG pipelines are a core slice of the graph. Compare pages and stacks show how these pieces (embeddings, a vector store, an orchestration framework) fit together.

## See also

- [Data retrieval category](/categories/data-retrieval)
- [Curated stacks](/stacks)

## Related terms

- [Embedding](/glossary/ai-and-llms/embedding.md)
- [Vector search](/glossary/ai-and-llms/vector-search.md)
- [Semantic search](/glossary/ai-and-llms/semantic-search.md)

[AI & LLMs](/glossary/ai-and-llms.md) · [All glossary terms](/glossary.md)

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon`](/api/graphcanon)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_