AEO, GEO, and AI Search Glossary
Search has split into two distinct systems. Google still ranks pages. AI platforms synthesize answers. Both require different optimization strategies and different vocabulary. This glossary covers both, current as of 2026.
When AEO was first defined, it meant optimizing for Google featured snippets and knowledge panels. That definition is still valid, but it now covers a much broader surface area. Google AI Overviews, ChatGPT Search, Perplexity, Gemini, Claude, and Microsoft Copilot all generate answers from the web, and each system uses different retrieval mechanisms, different authority signals, and different content evaluation criteria. Understanding the vocabulary is the prerequisite for understanding how any of this works.
What changed in AI search in 2025 and 2026
The most significant shift in 2025 was the normalization of AI-generated answers at scale. Google AI Overviews moved from limited rollout to widespread deployment across most query types. ChatGPT Search moved from beta to a default behavior for hundreds of millions of users. Perplexity grew from a niche tool to a mainstream alternative to Google for research-intent queries.
The practical consequence is that a brand can now rank on page one of Google and still be completely absent from the answers most AI systems generate for the same queries. These are separate visibility problems that require separate strategies. AEO addresses the Google answer layer. GEO addresses the generative AI layer. Neither replaces traditional SEO, which remains the foundation for both.
Most AI search systems now use Retrieval-Augmented Generation (RAG): they retrieve content from the open web at query time, evaluate source credibility and content relevance, and synthesize a response from selected passages. This means the open web directly shapes what AI systems say. Optimizing for RAG is the technical core of both AEO and GEO.
LLMs.txt: the shortcut that does not work
In 2024 and into 2025, a proposal circulated to solve AI visibility through a file called llms.txt, placed at the root of a website to instruct AI systems on how to crawl and use the site’s content. The proposal attracted significant attention in SEO circles. We tested it. The results were clear: llms.txt files have no measurable effect on AI answer inclusion.
The reason is structural. AI retrieval systems evaluate content credibility through signal breadth across the open web: backlinks from authoritative sources, consistent entity mentions, high-quality content structure, and clean technical access. A text file that declares intent does not substitute for any of these signals. AI systems do not take instructions from site owners about how to evaluate them any more than Google takes instructions from meta keywords tags.
The correct approach is building the actual signals that AI systems use: authoritative external citations, entity consistency, extractable content structure, and clean rendering. That is what the terms in this glossary describe.
How to use this glossary
Terms are organized alphabetically and searchable. Use the letter filter to jump to a section or type in the search box to find a specific term. Every term links to a dedicated definition page. If a term you are looking for is missing, the glossary is updated regularly. The terms below cover SEO, AEO, GEO, AI search mechanics, technical optimization, and content strategy vocabulary as they apply in 2026.
AEO is the practice of structuring content so that search engines and AI systems extract and present it as a direct answer. It targets Google featured snippets, knowledge panels, Google AI Overviews, voice search results, and AI-generated responses from platforms like ChatGPT and Perplexity. AEO requires direct question-and-answer content structure, Schema.org markup, and concise factual language aligned to how answer-generating systems retrieve and reuse content.
AEO focuses on being selected as a direct answer within Google’s traditional and AI-enhanced search features: featured snippets, knowledge panels, and AI Overviews. GEO focuses on being cited by standalone AI search platforms like ChatGPT, Gemini, and Perplexity, where no ranked list exists and only synthesized answers are returned. The two share significant technical and content overlap but target different systems with different retrieval mechanisms.
RAG (Retrieval-Augmented Generation) is the mechanism most AI search systems use to incorporate web content into generated responses. Rather than relying solely on training data, RAG systems retrieve content from the open web at query time, evaluate it for relevance and authority, and use selected passages to construct answers. Optimizing for RAG means writing self-contained content that answers specific questions directly, building authoritative external citations, and maintaining consistent entity signals across the web.
Yes. The glossary is maintained to reflect the current state of AI search, AEO, GEO, and SEO vocabulary. Terms introduced in 2024 and 2025, including AI Overviews, Generative Engine Optimization, Query Fan-Out, and others specific to the current AI search environment, are included. The glossary is updated as new terminology enters mainstream use in the industry.