AI is changing how people search, and SEO is evolving with it. This SEO for AI glossary makes the new terms simple and easy to understand. In just a few minutes, you can learn everything you need to know about SEO for AI and how to prepare your content for the future of search.

Atomic Content

Small, self-contained pieces of information designed to stand alone. AI models use these pieces to answer questions directly without needing full pages. Just like the LEGO pieces, atomic content can stand alone to answer a question or combine with other pieces to form full pages and resources for AI chats, such as ChatGPT, to use.

Query Fan-out

The way AI expands a single search into many sub-questions. Optimizing for this means creating content that can match different variations of the same query. In the world of traditional SEO, this concept is closely tied to establishing topical authority in your niche on your website, including FAQs and subcategories of the main topic areas.

Generative Search Optimization (GEO)

The practice of optimizing content so it appears in AI-powered search results, like Google’s AI Overviews, AI Mode, Claude, Copilot, Perplexity, Gemini, or ChatGPT answers. From measuring AI traffic and brand mentions in generative answers to influencing select AI prompts, GEO is a key building block of a modern SEO strategy.

LLMs.txt

A file (similar to robots.txt) proposed to tell large language models (LLMs) how they may crawl, use, or cite a website’s content. Even if AI engines don’t yet follow LLMs.txt, using it signals your preferences, prepares your site for future adoption, and positions your brand as forward-thinking.

LLMO (Large Language Model Optimization)

Similarly to GEO, the process of tailoring content so it is easily understood, retrieved, and cited by LLMs like ChatGPT, Gemini, or Claude.

Answer Engine Optimization (AEO)

Optimizing content for direct answers in search results, such as featured snippets, People Also Ask, and AI search answers.

Vector Analysis

A method used by AI and search systems to understand meaning through embeddings. It measures how close two pieces of content are in semantic space. Vector Analysis enables search engines to match your content by meaning, not just keywords, so make sure you leverage structured data, such as schema.org, on your web pages.

Content Chunking

Breaking content into smaller, labeled sections so AI can process and serve it more easily in response to user queries. Content Chunking makes content easier for AI to process, helping your site get cited in direct answers.

Have we missed anything important? Let us know in the comments!

We have also created an SEO-friendly AI Glossary JPEG below, which you can share on social media or add to your next blog post. Just please give us a shoutout as a source 😉  If you’d like to download this as a PDF, you can find it here.