# What is RAG? Retrieval-Augmented Generation, explained

> Grounding answers in retrieved sources.

Retrieval-Augmented Generation (RAG) is the pattern where an LLM, before answering, retrieves relevant documents from an index (vector store, search engine, or website) and grounds its answer in those documents. ChatGPT Search, Perplexity, and Google AI Overviews are RAG systems. Optimizing for RAG retrieval is the core of GEO and LLMO.

## How RAG differs from GEO, LLMO, Embeddings

A pure LLM generates from training-time weights only. A RAG system fetches fresh documents at query time and grounds the answer in them — which is why an article published last week can be cited by ChatGPT Search today.

## How Mentionwell handles RAG

- Per-article .md mirrors so retrievers ingest clean text rather than HTML noise.
- Embeddings indexed per article for semantic retrieval inside RAG pipelines.
- Stable canonical URLs and clean semantic structure so retrieved chunks make sense out of context.

## Frequently asked questions about RAG

### What is RAG?

Retrieval-Augmented Generation — an LLM pattern where the model retrieves relevant documents at query time and grounds its answer in them, instead of relying purely on training data.

### Which AI products use RAG?

ChatGPT Search, Perplexity, Google AI Overviews, Bing Copilot, Claude with web search, Gemini with grounding, and most enterprise AI assistants.

### How do I optimize content for RAG retrieval?

Clean semantic HTML, stable canonical URLs, Markdown mirrors, dense fact-rich paragraphs (so retrieved chunks carry meaning), and inline citations to authoritative sources.

## See also

- [GEO — Generative Engine Optimization](https://mentionwell.com/geo): Be the cited source.
- [LLMO — LLM Optimization](https://mentionwell.com/llmo): Be reachable, parseable, ingestible.
- [Embeddings — Vector Embeddings & Semantic Search](https://mentionwell.com/embeddings): Meaning as coordinates.
- [Grounding — Grounding](https://mentionwell.com/grounding): Tying answers to verifiable sources.


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Canonical URL: https://mentionwell.com/rag
Live HTML version: https://mentionwell.com/rag
Site index for AI ingestion: https://mentionwell.com/llms.txt
Full reference: https://mentionwell.com/llms-full.txt