LLM optimization, often shortened to LLMO, covers efforts to appear accurately in answers from AI tools built on large language models. The term overlaps heavily with generative engine optimization and answer engine optimization, and people often use them interchangeably. The practical work includes allowing the right crawlers, writing clear and self-contained content, publishing original facts and earning mentions on sites these systems draw from.
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Two ways a model can know you
A model may draw on what it absorbed during training, or it may retrieve live pages when answering. Retrieval is the part a site can influence most directly, since a page has to be reachable and useful when the system searches. Training influence is slower and much harder to attribute, because model makers do not publish how individual sites affect a model.
Crawler access also needs care, because companies may use separate crawlers for separate purposes. OpenAI, for example, documents OAI-SearchBot for surfacing sites in ChatGPT search and GPTBot for content that may be used in model training, with independent robots.txt controls for each.
What the work involves
- Check robots.txt and CDN rules so that intended crawlers are not blocked by accident
- Put key content in plain text that does not depend on client-side rendering
- Write passages that state their subject and make sense when quoted alone
- Publish original research, clear product facts and honest comparisons
- Earn mentions on credible third-party sites, reviews and communities
- Check a fixed set of prompts over time, and read the answers for accuracy
What is proven and what is not
Google says there are no additional requirements or special optimizations for AI Overviews and AI Mode, and that you do not need AI text files or special schema. Ordinary SEO fundamentals still apply there. The llms.txt format remains a proposal, and publishing one does not guarantee that any assistant will use it. AI answers can also vary between runs, so treat a single answer as a sample. Be cautious of any tool or agency that promises citations.
Example
Correcting an outdated answer
Hypothetical analytics SaaS Pellwater found that assistants often described it as lacking SSO, a feature it had added some time earlier. Its security page mentioned SSO only inside an image, and an old review site carried the outdated claim.
The team put the facts in plain text, updated its pricing and docs pages, and asked the review site for a correction. A person kept checking the same prompts monthly and recorded answers without assuming any single change had caused an improvement.
Common mistakes
Blocking every AI crawler by default
A blanket rule can also block search-focused crawlers. Decide per crawler, based on what each one does.
Believing a single file will fix it
An llms.txt file does not replace crawlable content, clear facts or third-party reputation.
Publishing machine-written pages at scale
Mass-produced pages with little value can run into Google's scaled content abuse policy, and they rarely offer facts worth citing.
Frequently Asked Questions
Mostly in name. LLMO stresses the underlying models, GEO stresses generative search engines, and the practical work overlaps almost entirely.
Google says no. Its guidance says there are no special requirements or optimizations for AI Overviews and AI Mode beyond being indexed and eligible to appear with a snippet.
No. Answers vary and depend on systems outside your control. You can improve your chances and measure over time, but no one can honestly promise a citation.
Keep reading
- Generative Engine Optimization: GEO is the work of making a brand and its pages easy for AI search tools like ChatGPT, Perplexity and Google AI Overviews to find, understand and cite.
- Answer Engine Optimization: AEO is optimizing content so search engines and AI assistants can lift a direct answer from it, in a snippet, a voice reply or an AI-written summary.
- AI Citation: An AI citation is a link or source reference that an AI search tool shows beside its answer to point to the page it drew on.
- llms.txt: llms.txt is a proposed Markdown file at a site's root that gives AI tools a short summary of the site and links to its most useful pages.
- Agentic Browsing: Agentic browsing is when an AI model operates a web browser for a user, reading pages, clicking, scrolling and filling in forms to complete a task.
- AI Agents: AI agents are systems where a language model plans and takes actions with tools, such as searching, browsing, filling forms or calling APIs, to finish a task.
- AI Answer Sentiment: AI answer sentiment is how positively, neutrally or negatively AI assistants describe your brand, and which strengths and weaknesses they repeat.
- AI Answer Variability: AI answer variability is the way the same prompt can produce different answers, sources and brand mentions across runs, users, places and time.
