Ai Search Visibility Seo Geo Aeo
Ai Search Visibility Seo Geo Aeo brings together 81 resources with credited original sources. Use prerequisite levels to find learning material; unrated entities remain available in the full collection.
81 resources · page 3 of 3
From Discovery to Influence: A Guide to GEO
Microsoft Advertising playbook for retailers on the product feeds, crawled pages and off-site data AI assistants use when recommending products.
Optimizing Your Content for Inclusion in AI Search Answers
Microsoft's October 2025 explanation of how Copilot splits pages into chunks for answers, with do and avoid lists for headings, Q&A blocks, tables and schema.
llms.txt in Mintlify
by Mintlify
Mintlify docs on the llms.txt and llms-full.txt files it hosts automatically for every documentation site, and how to override them.
gtm-engineer-skills
by Voyage AI
Agent skills that each produce a GTM file, such as keyword and GEO prompt clusters, an AEO audit, a backlink plan or website code fixes.
Product feed specification
by OpenAI
OpenAI's list of required and optional product feed fields that merchants submit so their products can be shown and bought inside ChatGPT.
Perplexity Crawlers
by Perplexity
Perplexity's page on PerplexityBot and Perplexity-User, with their user-agent strings, published IP ranges and how robots.txt applies to each.
aeo-skills
by psyduckler
Agent skills for prompt research and tracking a brand in Gemini AI Overviews, ChatGPT and Perplexity, storing every raw response as JSON.
The Website Specification
Joost de Valk's sourced reference of web standards from HTML head tags to robots.txt, llms.txt and security.txt, also queryable through an MCP server.
geo-seo-claude
Claude Code skill with /geo commands for AI crawler access, citability scoring, llms.txt, schema, brand mentions and technical SEO audits of a URL.
Evaluating Verifiability in Generative Search Engines
by Nelson F. Liu, Tianyi Zhang, Percy Liang
Human audit of four generative search engines measuring how many answer sentences are fully supported by their citations and how many citations support them.
Manipulating Large Language Models to Increase Product Visibility
by Aounon Kumar, Himabindu Lakkaraju
Harvard study showing that an optimised text sequence added to a product page raises how often an LLM lists that product as its top recommendation.
Ranking Manipulation for Conversational Search Engines
by Samuel Pfrommer, Yatong Bai, Tanmay Gautam, Somayeh Sojoudi
Study of how text injected into product pages changes the order in which conversational search engines rank and cite those products.
Adversarial Search Engine Optimization for Large Language Models
by Fredrik Nestaas, Edoardo Debenedetti, Florian Tramèr
ETH Zurich paper on preference manipulation attacks, where web pages or plugin docs steer an LLM to promote one product and discredit rivals.
C-SEO Bench: Does Conversational SEO Work?
by Haritz Puerto, Martin Gubri, Tommaso Green, Seong Joon Oh, Sangdoo Yun
Benchmark that tests published conversational SEO methods across several domains and with many competing actors, asking whether they raise visibility.
Generative Engine Optimization: How to Dominate AI Search
by Mahe Chen, Xiaoxuan Wang, Kaiwen Chen, Nick Koudas
University of Toronto experiments comparing which sources AI search engines and Google cite across verticals, languages and query types.
What Generative Search Engines Like and How to Optimize Web Content Cooperatively
by Yujiang Wu, Shanshan Zhong, Yubin Kim, Chenyan Xiong
Carnegie Mellon paper introducing AutoGEO, which learns generative engines' content preferences and rewrites pages to match them.
E-GEO: A Testbed for Generative Engine Optimization in E-Commerce
by Puneet S. Bagga, Vivek F. Farias, Tamar Korkotashvili, Tianyi Peng, Yuhang Wu
Testbed of e-commerce queries for measuring how rewriting product descriptions changes their ranking in generative engine answers.