Glossary
The words of GEO
Twelve concepts to understand how an AI chooses its sources.
- GEO
- Generative Engine Optimization: optimising a brand's visibility in the answers of generative engines. Related terms: AEO, LLMO, “SEO for AI”.
- LLM
- Large Language Model: a language model trained on vast corpora (GPT, Gemini, Claude, Mistral…), able to understand and generate text.
- RAG
- Retrieval-Augmented Generation: the model first retrieves documents (web, internal database), then writes its answer based on them.
- Grounding
- Anchoring an answer in verifiable sources, displayed as links. This is the mechanism that produces citations.
- Query fan-out
- Breaking a question down into sub-queries run in parallel, used in particular by Google's AI Mode.
- Embedding
- Representation of a text as a vector: two texts with close meanings have close vectors. This is the basis of semantic search.
- Chunk
- A passage of a page indexed and retrieved separately. A good chunk can be understood on its own, without the rest of the page.
- Reranking
- A second, finer ranking of the retrieved passages, just before the answer is generated.
- Entity
- An unambiguously identified object (company, person, place) in a knowledge graph, linked to its attributes and profiles (
sameAs).
- Share of model
- The share of answers from a panel of prompts in which your brand is cited or recommended. The AI equivalent of share of voice.
- Hallucination
- False information produced by a model (opening hours, prices, services). It is corrected by making the right information consistent and plentiful across sources.
- llms.txt
- A Markdown file placed at the root of a website, summarising for language models what they need to know and where to find the key pages.