> ## Documentation Index > Fetch the complete documentation index at: https://docs.makemerank.ai/llms.txt > Use this file to discover all available pages before exploring further. # Methodology > How MakeMeRank measures and evaluates AI visibility ## Four Guiding Questions The MakeMeRank methodology is based on four core questions: ### 1. Are the foundations in place? Check: Is your website technically readable for AI models? Do models find you in brand searches? This includes: * llms.txt * robots.txt configuration for AI bots * Structured data (Schema.org) * Clear, crawl-friendly content ### 2. Are you recommended without your brand name? The most honest visibility test: Do models mention you in general topics too, without explicitly searching for you? This corresponds to the **Visibility** score. ### 3. What are the next steps? From the analysis we derive prioritized actions – sorted by impact and effort. ### 4. What content is missing? Report gaps are turned into concrete content suggestions – LinkedIn and blog – aligned with your analysis. ## Research Foundation MakeMeRank is based on: * **Princeton / IIT Delhi** – GEO: Generative Engine Optimization (2023): Strategies like citability, statistics, source citations can increase visibility in LLM answers by up to 40%. * **Microsoft** – From Discovery to Influence: A Guide to AEO and GEO (2025): Snippability patterns, content structure, robots.txt. * **Vercel** – How We're Adapting SEO for LLMs and AI Search (2025): Passage quality, llms.txt, structured answer blocks. Details and links: [GEO Wiki – Research & Sources](https://makemerank.ai/wiki#research-sources). ## Snippability Patterns Answers must be "liftable" – individually understandable and citable. Proven patterns: * **Short Answer Block** – H2/H3 as question, 1–2 sentence answer, then details * **Q\&A Block** – Clear question-answer pairs * **Comparison tables** – e.g. Model A vs. Model B * **Step-by-step How-To** – Numbered guides * **Glossary entries** – Definition + short explanation Hidden content in tabs/accordions: Microsoft warns that AI systems often don’t reliably use hidden content. Core theses should be directly visible.