What is LLM optimization (LLMO)?

LLM optimization, often shortened to LLMO and sometimes called LLM SEO, is any effort to change what large language models such as ChatGPT, Claude or Gemini say about a brand, product or topic. It overlaps heavily with GEO and AEO. The labels differ mostly in who coined them, as our breakdown of AI search acronyms shows.

Treat LLMO as a goal, not a method. The term says what you want. It does not say what works. For the full picture of which parts you can move, read the guide to what LLM optimization can and can't influence.

An AI answer about your brand comes from one of two places, and the difference decides what you can do.

The parametric layer

This is what the model learned in training. It is frozen at a knowledge cutoff. OpenAI's model documentation lists a February 16, 2026 cutoff for the GPT-5.6 family and August 31, 2025 for ChatGPT's Instant path. Nothing you publish today reaches an answer the model gives without searching.

The retrieval layer

When the model searches, three gates apply in order:

1. Access. Can the crawler fetch your page? Robots.txt rules are per bot. Vercel found that ChatGPT and Claude crawlers download JavaScript files but don't execute them, so content that only appears after JavaScript runs can be invisible.
2. Retrieval. Does your page enter the model's context for this query? The engine rewrites the user's prompt into its own searches, which you can't see.
3. Generation. Which retrieved pages get used and cited? The model decides.

Our explainer on how large language models find content walks through each gate.

Retrieval is discretionary on every platform as of October 2026. ChatGPT searches at the model's discretion, and Claude.ai search is a user toggle. So any answer about you is either a stale memory or a live lookup, and you usually can't tell which.

The live lookup is the only part you can affect, and the evidence there is narrow. A 2026 critical survey of 45 GEO studies (arXiv 2607.14035) found that no reviewed technique shows a stable, cross-platform causal effect on discoverability. What holds up is removing blocks:

• Allow the retrieval bots you want, such as OAI-SearchBot for ChatGPT search, in robots.txt.
• Confirm your CDN or firewall doesn't return 403 to crawlers that robots.txt allows.
• Put prices, product names and key facts in the raw HTML.
• Remove accidental noindex, nosnippet or data-nosnippet tags.

For Google, eligibility for AI Overviews and AI Mode needs only that a page is indexed and eligible to show with a snippet, with no additional requirements. The comparison of rewrites versus access fixes lays out the evidence side by side.

• "Publish an llms.txt." Google says Search ignores it, and no engine documents reading it. See whether any engine reads llms.txt.
• "Add schema to get cited." Ahrefs' matched test of 1,885 pages found AI Overview citations fell 4.6% after adding schema. Existing markup does no harm, but it is not a lever for AI answers.
• "Rewrite in an AI-friendly style." Google says you don't need to write a specific way for generative AI. C-SEO Bench found most rewriting methods ineffective and often harmful.
• "Allow GPTBot so ChatGPT finds you." GPTBot governs training only. OpenAI says each bot setting is independent.
• "LLMO delivers 40% more visibility." That figure came from a lab engine with sources already loaded into context.

• Citation versus mention: a link to your page versus your name in the answer text. See citations vs mentions.
• Third-party presence: when engines cite other sites, being named on those sites is associated with appearing in the answer. Compare your own site versus third-party mentions.
• Knowledge cutoff: the date after which a model's training memory knows nothing.
• Grounding: an answer built from pages the engine fetched live.

Is LLMO different from GEO or LLM SEO? Not in any meaningful way. All three describe efforts to shape what AI engines say or cite. LLMO stresses the model, GEO stresses the generative engine, and LLM SEO borrows search vocabulary. The evidence applies the same way regardless of label: access levers hold up, and content tricks measured inside a model's context have not shown durable effects on real-world discoverability.

Can I change what ChatGPT already knows about my brand? Not this quarter. Training memory is frozen at a cutoff, and any effect from new training data lands a full model generation later, if at all. Nobody has measured whether allowing training crawlers improves brand recall. What you can affect is the search path: make sure OAI-SearchBot can reach your pages and that key facts sit in the HTML.

Does ranking well in Google help with LLMO? Partly, and only for some engines. Ahrefs found that 38% of AI Overview citations came from pages in the top 10 results, using its March 2026 method. Google's AI features also require a page to be indexed and snippet-eligible. Other engines use different indexes, so check each one separately rather than assuming Google rank carries over.

How do I check where I stand? Start with access: read your robots.txt per bot, request a page as each AI crawler to see if your firewall blocks it, and compare raw HTML with the rendered page. Then ask the engines buyer questions directly. The free checker on this site runs up to three buyer questions and shows whether AI names you, as a one-time snapshot.