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
Ship RAG-optimized articles automatically
Mentionwell handles RAG on every published article — alongside the other six optimization targets in this glossary — so you don't have to think about it per post. Drop a domain, approve the first headline, watch the pipeline run.