In One Sentence
RAG (Retrieval-Augmented Generation) is a setup where, when generating an answer, an LLM searches external documents or databases and incorporates those results into the answer.
What does this look like in practice?
For example, when ChatGPT is asked, "What are the latest trends in the AI market?":
[Traditional LLM]:
Only knows up to its training-data cutoff (e.g. one year ago),
so it answers with stale information or "I don't know"
[AI search using RAG]:
1. Searches the web in real time (retrieval)
2. Obtains related information
3. Generates the answer based on it (generation)
→ Returns an answer informed by the latest information
This is the core technology behind Perplexity, SearchGPT, and Gemini AI Overview.
Why it matters in a GEO context
- AI search engines are all RAG-based
- "Being cited in the retrieval phase" is the essence of GEO
- Even if not included in training data, being findable on the web makes you a citation candidate (new articles are immediately eligible for citation)
The RAG flow (from a GEO perspective)
User question
↓
LLM judges "this question needs a web search"
↓
Candidates fetched from the crawler (search index) ← * The main battleground for GEO tactics *
↓
Answer generated from the fetched information
↓
Answer returned to the user (with sources cited)
Whether your company is selected at step 3, "candidate retrieval", decides the success or failure of GEO tactics.
When you build your own RAG system
Separately from GEO, there are growing numbers of cases where companies build RAG systems on top of their own data (internal ChatGPT, customer-support AI, etc.). The technique is the same, but the focus of GEO is "being cited by external AI search".