Generative Engine Optimization (GEO), explained for B2B

Key terms

• AI answer engine — a product that responds to questions with a generated answer rather than a list of links, such as AI assistants and AI-generated summaries in search results.
• Retrieval — the step in which a system searches an index or the web for current information to inform an answer.
• Citation — a source an answer names or links to.
• Entity — a distinct thing (a company, product or category) that systems try to recognize and describe consistently.

How answers are assembled

Products differ and change often, but answers that draw on current information generally follow a pattern:

1. Prompt. A user asks a question.
2. Retrieval. The system searches indexes or the web for relevant sources.
3. Evaluation. Sources are weighed for relevance and credibility.
4. Synthesis. An answer is written from the selected material.
5. Citation. Some sources are named or linked.

Separately, a model’s training data shapes its general understanding of brands and categories. That influence is slower and much less direct.

What B2B companies can influence

Retrievability. Content that search systems cannot crawl or render cannot be retrieved. Server-render core content, keep pages fast and stable, and decide your AI crawler access policy deliberately.

Clarity. State what the product is, who it is for and what problem it solves in plain language on key pages. Use consistent names for your company, products and category everywhere they appear.

Corroboration. Systems weight what credible third parties say. Coverage in relevant publications, analyst material, reviews, community discussion and partner directories all contribute.

Specificity. Original data, clear frameworks, concrete comparisons and attributed expertise are more useful to quote than generic overviews.

Structure. Clear headings, explicit definitions, direct answers and structured data that truthfully describes the page make content easier to interpret.

What cannot be controlled

• the exact wording of any answer,
• which sources are selected for a specific prompt,
• changes to models, products and retrieval systems,
• personalization and conversational context.

Be wary of anyone who promises guaranteed placement.

About llms.txt

llms.txt is a proposed convention for giving language models a curated summary of a site. It is not an established standard, and there is limited public evidence that major AI products rely on it. It is inexpensive to add but should not be treated as a strategy.

Measuring AI search visibility

1. Build a prompt set. Problem questions, category questions, comparisons and “which vendor” questions your buyers are likely to ask.
2. Test repeatedly across products. Answers vary between sessions, so look for patterns, not single results.
3. Record whether you are mentioned, how you are described, whether the description is accurate and which sources are cited.
4. Triangulate with AI referral traffic, branded search and “How did you hear about us?” responses.

Treat results as directional evidence. Improvements tend to show up gradually, across many observations.

A starting checklist

• Key pages render their content on the server
• Crawler access policy reviewed deliberately
• Clear definition of company, product and audience on core pages
• Consistent naming across site and third-party profiles
• Structured data that matches visible content
• A plan to earn relevant third-party references
• A baseline prompt set, re-tested on a schedule

See how we approach this work on AI & LLM search visibility.