Search "LLM optimization" and you get a wall of results about quantization, KV-cache tuning and inference latency. That is the engineering meaning: make a model cheaper and faster to run. If you own a brand, a pipeline or a marketing budget, that is not your problem.
Your problem is narrower and more expensive. A buyer types "best [your category] for a 50-person team" into ChatGPT, Gemini, Claude or Perplexity. The answer names three companies. None of them is you. The buyer never opens a search results page, never sees your ad, never lands on the comparison post you spent three weeks on. The deal is shaped before you know it exists.
That is LLM optimization in the marketing sense: influencing how large language models describe, rank and recommend your brand. You will also hear it called generative engine optimization (GEO), answer engine optimization (AEO) or LLM SEO. The labels overlap. The work is the same.
Two meanings, one phrase
Before you brief anyone, settle the vocabulary so your ML lead and your content lead stop talking past each other.
Model optimization is an engineering task. Pruning, distillation, quantization, prompt caching, batching. The goal is throughput per dollar. The owner is infrastructure.
LLM optimization, as used here, is a go-to-market task. Entity clarity, citable content, third-party corroboration, crawler access and ongoing measurement. The goal is a higher rate of accurate brand mentions in AI answers. The owner is marketing, with help from SEO and comms.
Everything below is about the second one.
Why the timing matters
OpenAI put ChatGPT at 900 million weekly active users in early 2026, more than double the figure a year earlier. Gemini ships inside Google Search and Workspace. Perplexity has made itself the default for research-heavy queries. Every one of those surfaces answers the "which vendor should I pick" question without showing ten blue links.
Two things follow from that. First, a growing share of your category demand never touches a results page you can rank on. Second, the answer a model gives is repeatable. Ask the same buying question fifty times and the same three or four brands show up most of the time. Being on that list is a position. Being off it is a leak in your funnel that your analytics cannot see, because there is no click to attribute.
How a model decides who to name
Models pull from two places, and each responds to different levers.
The first is what the model learned during training. Every description of your company across the open web, Wikipedia, review sites, press, directories and forums got compressed into weights. If those descriptions are sparse, inconsistent or outdated, the model either hallucinates your positioning or skips you. You cannot patch training data on demand. You can only make the next crawl of the web more consistent than the last.
The second is live retrieval. ChatGPT search, Perplexity, Gemini grounding and Google AI Mode fetch pages at answer time and quote them. This path behaves more like classic search, with a twist: the model wants a passage it can lift and attribute, not a page it can rank. Direct answers, clean structure and explicit numbers win. Long preambles lose.
Most brands are invisible on the first path and mediocre on the second. Fixing that is the job.
The LLM optimization framework
Five parts. Do them in order.
1. Get your entity straight
Write one sentence that says what you are, who you serve and where you operate. Use that sentence, word for word, on your homepage, your About page, LinkedIn, Crunchbase, G2, your Wikipedia or Wikidata entry if you have one, and every press boilerplate. Models learn by repetition across independent sources. Ten slightly different self-descriptions teach a model nothing. One description repeated in thirty places teaches it exactly what to say.
Test it. Ask each major model "What is [brand]?" and "What does [brand] do?" If the answers are wrong, vague or dated, the entity work is not done.
2. Write answer-shaped pages
Retrieval models extract passages. Give them passages worth extracting.
Lead with the answer. The first 80 words of any page should resolve the question in the title.
Use question-form H2s that mirror how buyers actually ask.
Put two or three specific, sourced numbers on every important page. Models cite numbers.
Add FAQ and Organization schema. It is not magic, but it removes ambiguity.
Keep tables and lists real. A comparison table with honest criteria gets quoted. A wall of adjectives does not.
3. Earn corroboration you do not control
This is the part most teams skip, and it is the part that moves the needle. Models weight independent sources above your own site. Run your twenty most important buying prompts through Perplexity and note which domains it cites. Those are the pages that currently define your category. Get on them. That means review profiles with fresh reviews, inclusion in the "best tools for X" roundups that keep getting cited, honest Reddit and community presence, and trade press that describes you the same way you describe yourself.
Think of it as a citation gap analysis. Your competitor is on eight of the ten pages the model trusts. You are on two. Close the gap and the answers change.
4. Open the door technically
Check robots.txt for GPTBot, ClaudeBot, PerplexityBot and Google-Extended. Plenty of sites blocked them in 2023 and forgot. Server-render your key pages; several AI crawlers still do not execute JavaScript well. Keep canonical tags clean so the model does not split your authority across duplicates. An llms.txt file is cheap to add, though evidence that models use it is thin, so treat it as optional.
5. Measure the rate, not the rank
Rank in an AI answer is close to random between runs. Mention rate is not. Build a prompt set of 30 to 100 questions your buyers ask, run it across ChatGPT, Gemini, Claude and Perplexity on a schedule, and track four numbers: how often you are mentioned, your share of voice against named competitors, the sentiment of the mention, and which of your URLs get cited. That is the dashboard. Everything above feeds it.
A 30-day starting plan
Week one, audit. Run the prompt set, record the baseline, list the domains being cited.
Week two, fix the foundations. Entity sentence everywhere, crawler access, schema on the top ten pages.
Week three, rewrite. Take the five pages closest to your buying prompts and restructure them to answer first.
Week four, corroborate. Pitch three roundup authors, refresh two review profiles, publish one comparison page with real criteria. Then re-run the prompt set and compare.
You will not go from invisible to dominant in a month. You will see the mention rate move, and you will know which lever moved it.
Mistakes that waste the quarter
Treating this as a one-off project. Models refresh, competitors publish, answers drift. This is a monitoring discipline, not a launch.
Optimizing for ChatGPT alone. Gemini reaches a different audience through Google. Perplexity cites differently. Claude is increasingly the model behind B2B research assistants. Track all of them or you are guessing.
Chasing rank. Two runs of the same prompt can reorder the list. Report mention rate and share of voice instead, or you will be explaining noise to your CFO.
Pasting "AI-friendly" boilerplate onto pages. Models are trained on the whole web. They recognize filler. Specifics beat signals every time.
Where to start today
Open ChatGPT, Gemini and Perplexity. Ask each one the question your best customer asked before they found you. Write down who gets named. If you are not on the list, you now have a baseline and a reason.
If you would rather not do that by hand every week, LLM Search Console runs your prompt set across the major models on a schedule and reports mention rate, share of voice, sentiment and citations in one place. Start with a free audit and see what the models are already saying about you.