Guides

Learn GEO

1. Overview

   LLM SEO: a working guide, from first measurement to proof it worked

   The whole loop in one page — what to measure, how to read it, how to diagnose an absence, what to change, and how to tell a real difference from noise — with the eight lessons that cover each step.

2. Lesson 1

   What is answer engine optimization? AEO, GEO and LLM SEO compared

   AEO, GEO and LLM SEO are three labels for one job — getting named in AI-generated answers. Here is what each term means, which surface it refers to, and how the work differs from SEO.

3. Lesson 2

   How to build an AI visibility question set that measures something

   The questions you ask decide what your AI visibility number means — here are the rules our pipeline enforces in code, the failure modes they catch, and how set size feeds into the confidence interval.

4. Lesson 3

   How to track brand mentions in ChatGPT (without fooling yourself)

   A repeatable way to check whether ChatGPT names your brand when buyers ask about your category, with the four things to record per answer, a worked confidence interval from one of our own runs, and the mistakes that produce numbers you cannot trust.

5. Lesson 4

   Reading an AI visibility report: mention, position, citation, share of voice

   What each number in an AI visibility report answers, what it cannot answer, and what sits in its denominator, with one of our own measurement runs read column by column including the columns that came back empty.

6. Lesson 5

   Why the AI answer left your brand out, and what the data can honestly tell you

   A diagnostic method for a zero mention rate, worked on one of our own runs where the brand was named in none of three answers, separating what the competitor and citation columns show from what they only suggest.

7. Lesson 6

   How to make content citable: writing passages an AI assistant can lift

   A citable passage survives being pulled off the page. Here are the five edits that produce one, four before-and-after rewrites from our own site, and an honest split between what the research supports and what is only our hypothesis.

8. Lesson 7

   AI crawlers, robots.txt and llms.txt: what the vendors actually document

   The AI crawler user agents OpenAI, Anthropic, Perplexity and Google publish, what allowing or blocking each one trades away, and what evidence there is for llms.txt.

9. Lesson 8

   Did it work? Re-measuring AI visibility after a change

   Three identical runs of the same question set on the same morning, with nothing changed between them, and what their disagreement tells you about when a before-and-after comparison is allowed to mean anything.

More guides

• 2026-09-20 · Measured

  How we measure AI visibility: engines, samples, metrics and limits

  The method behind every number RedClaw Labs publishes — which engines we query, how the questions are built, how mention rate and share of voice are calculated, and what the numbers cannot tell you.

• 2026-09-20 · Guide

  AI visibility tools: what each vendor publishes about its own method

  A comparison of seven AI visibility tools on four checkable criteria — engines covered, sampling method, repeats and interval, and public pricing — built only from what each vendor publishes on its own pages. We sell one of them, so there is no ranking here.

• 2026-09-20 · Guide

  AI brand monitoring: how it differs from social listening, and what to measure

  AI brand monitoring watches what assistants say about you, not what people post about you. Here is the difference between the two product categories, the four metrics worth tracking, and the denominator each one needs.