How do LLMs actually decide which businesses to surface?
Every AI answer that names a company drew that name from one of three places. Most LLM optimization guides blur them together; you can't, because the work for each is different.
| Path | What it is | How current | Can you influence it? | The work |
|---|---|---|---|---|
| 1. Training data | Patterns the model memorized during pre-training | Stale — frozen at the training cutoff | Slowly, via corroborated public mentions | Entity consistency, third-party coverage |
| 2. Retrieval (RAG) | Documents the engine fetches and reads at answer time | Live — your current pages | Yes — the highest-leverage path | Crawler access, extractable answer-first content |
| 3. Search grounding | A classic search index (Google, Bing, Perplexity's own) consulted mid-answer | Live — follows search rankings | Yes — via ordinary SEO done well | Rankings for question queries, citations |
The strategic insight of this whole guide sits in that table: path 1 is slow, path 2 is yours to win this quarter, and path 3 means traditional SEO never stopped mattering. A serious LLM SEO services program works all three simultaneously — but if you're doing this yourself, work them in the order below.
Path 1: Training data — become something the model already "knows"
When you ask an assistant a question and it answers without citing anything, it's answering from memory: statistical associations absorbed from billions of pages before its training cutoff. Nobody — no agency, no amount of budget — can edit those weights. What you can do is shape what the next model generation learns, because every frontier lab retrains on a fresh crawl of the web.
The work for this path:
- Fix your entity everywhere. One exact business name, one description, one category, one location story — identical across your site, schema markup, LinkedIn, directories, and data aggregators. Models learn entities from repetition; contradictions teach the model to hedge about you.
- Earn mentions in crawled, durable places. Industry publications, local news, association listings, podcast show notes, well-moderated forum threads. Training crawls weight pages that survive and get referenced. A single real profile in a trade publication outweighs fifty syndicated press releases.
- Associate your brand with your category in text. The phrase pattern "AISEO USA, an AI SEO agency" appearing consistently across independent sources is literally how a model learns what you are. Get third parties describing you in the same words you use.
Be honest with yourself about timelines here: this path pays off in model generations, not weeks. That's why it's first in priority order but last in expected results — start it now, then move your energy to the paths that respond this month.
Path 2: Retrieval — win the short list at answer time
This is the highest-leverage path in any LLM SEO strategy, because it's live. Modern assistants run retrieval-augmented generation (RAG): the engine converts your question into an embedding, pulls the closest-matching passages from an index into the model's context window, and writes a grounded answer from those passages. The sources it grounds on are the sources it cites. Change your pages today, and you've changed what the model reads tomorrow.
Two jobs decide this path.
Job one: be physically retrievable
Before optimizing a single sentence, confirm AI crawlers can actually reach you. We say this from experience, not theory: on our own domain we discovered a Cloudflare WAF rule — "Block AI bots by User Agent" — silently returning 403 to every AI crawler (GPTBot, OAI-SearchBot, PerplexityBot, Claude's crawlers) and, because it matched user-agent strings regardless of IP, to real Googlebot and Bingbot too. Nothing looked broken. The site loaded fine for humans while being invisible to every machine that mattered. We only found it by reading the WAF rulesets through the API.
Your checklist: audit CDN/firewall rules and bot-protection settings, not just robots.txt; explicitly allow GPTBot, OAI-SearchBot, ChatGPT-User, PerplexityBot, Google-Extended, and ClaudeBot; then verify with real fetches using those user agents. Assume nothing — "AI bot blocking" is now a one-click feature on major CDNs, and plenty of businesses turned it on in 2023 without understanding what it would cost them in 2026.
Job two: be worth quoting once retrieved
Retrieval happens at the passage level. The model doesn't reward your domain; it lifts a chunk of text that answers the question on its own. So structure every important page for extraction:
"Optimizing for LLMs is closer to pitching a careful journalist than gaming an algorithm. The journalist quotes whoever hands them a number, a name, and a date in one clean sentence. Write every page so a machine on deadline can quote you." — Thomas, Founder of AISEO USA
Path 3: Search grounding — why classic rankings still feed AI answers
Here's the part of LLM search optimization that surprises people: several major engines don't primarily use a private AI index — they ground answers in an ordinary search index, live, mid-answer. Which means your traditional rankings are an input to AI answers, even as they stop being the destination.
The evidence that the click is migrating into the answer layer is stark. Pew Research Center tracked real user behavior and found that when a Google results page includes an AI summary, users click a traditional result on just 8% of visits, versus 15% without one — and about one-in-five searches already produced an AI summary by March 2025. Semrush's ongoing study shows how volatile and how large this surface is: AI Overviews appeared on 6.49% of queries in January 2025 and peaked at 24.61% by July. The traffic isn't disappearing — it's being re-routed through a synthesis layer that cites a handful of winners. Pew found 88% of AI summaries cite three or more sources, so the game is being one of the several sources the layer trusts, not the single blue link that wins the click.
Grounding works differently per engine, and the work follows:
The practical takeaway: don't abandon SEO for "AI optimization." Path 3 is SEO — aimed at question queries, answer-ready pages, and citation-worthiness rather than position one for its own sake.
The LLM SEO strategy in order: your first 90 days
Everything above compresses into a sequence. This is how to optimize for LLMs without boiling the ocean:
- Week 1 — Baseline. Run the same 20–30 buying prompts ("best [your category] for [your customer]", "[your brand] reviews", "alternatives to [competitor]") across ChatGPT, Gemini, and Perplexity. Log who gets named and cited. A free AI visibility audit does this for you and shows the gaps.
- Week 1–2 — Unblock the crawlers. CDN rules, WAF rules, robots.txt, bot management. Verify with real fetches. (This is the single most common silent killer we find.)
- Week 2–4 — Fix the entity layer. Consistent name/description/category everywhere; Organization and FAQPage schema; a real author with credentials on every page.
- Month 2 — Rewrite the money pages for extraction. Question H2s, answer-first blocks, one verifiable statistic with a linked source per major claim, comparison tables, visible update dates.
- Month 2–3 — Build corroboration. Third-party mentions, category associations, the durable coverage that feeds both retrieval trust today and training data tomorrow.
- Month 3 and ongoing — Re-measure monthly. Same prompt panel, same engines. Track share of voice, not vanity rankings. Iterate on what each engine actually cited.
That sequence is the skeleton of what we run as a managed LLM SEO services engagement — the difference is depth, tooling, and having done the failure-hunting (like the CDN block above) enough times to find problems fast.
Frequently Asked Questions
What is the difference between LLM SEO and traditional SEO?
Traditional SEO optimizes pages to rank in a list of results; LLM SEO optimizes your content and entity so language models retrieve, trust, and cite you inside a generated answer. The levers shift from keywords and links toward evidence density, extractable passages, entity consistency, and crawler access. The disciplines overlap heavily — search grounding means good SEO still feeds AI answers directly.
How do I optimize my website for LLMs?
Work the three paths in order: verify AI crawlers (GPTBot, OAI-SearchBot, PerplexityBot, Google-Extended) aren't blocked by your CDN or firewall; restructure key pages with question-phrased headings, direct 40–60 word answers, statistics with linked sources, and schema markup; then make your business entity consistent across the web so models can identify you confidently. Measure with a fixed prompt panel monthly.
Can you get your business into an LLM's training data?
Not directly — no one can edit a trained model's weights. You influence the next training run by earning consistent, corroborated mentions in durable, crawlable places: industry publications, news coverage, directories, and forums. Expect this path to pay off in model generations (many months), which is why live retrieval and search grounding are the near-term levers.
Which LLM should I optimize for first?
Start where your buyers are. ChatGPT's 900 million weekly active users make it the default first target for most US businesses, with Google's AI surfaces close behind given AI Overviews' 2 billion monthly users. The good news: answer-first, evidence-dense pages improve your odds across every engine at once.
Does traditional SEO still matter for LLM visibility?
Yes — more than most AI commentary admits. ChatGPT grounds answers with search signals including Bing, Gemini and AI Overviews ground in Google Search, and Perplexity crawls the open web. If your pages don't rank for the question phrasings buyers use, the grounding layer never sees them. LLM search optimization redirects SEO effort toward question queries and citation-worthiness; it doesn't replace it.
Do I need an llms.txt file for LLM SEO?
It's optional and low-priority. llms.txt is a proposed convention for pointing models to your key content, but adoption by the engines remains unproven, so treat it as a cheap experiment, not a strategy. What's not optional is the inverse problem: making sure firewalls, CDNs, and robots.txt aren't blocking AI crawlers — a silent, common failure that outweighs any helper file.
How long does LLM SEO take to show results?
Retrieval-path changes move fastest: pages rewritten for extraction can appear in AI answers within weeks of being recrawled, and unblocking crawlers acts immediately. Entity and corroboration work settles over a few months as third-party sources update. Training-data influence takes model generations. No one can guarantee a specific AI placement — disciplined work raises the probability across every engine and gives you a scoreboard.
How do I measure whether my LLM SEO strategy is working?
Build a fixed panel of 20–30 real buying prompts and run it monthly across ChatGPT, Gemini, and Perplexity. Track three numbers: how often you're named, how often you're cited as a source, and who gets named instead of you. That's share of voice in the answer layer — a more honest KPI than rankings, because it measures the surface where decisions now happen.
16 years in digital marketing, focused on AI SEO, GEO, AEO and local search. Every claim in this article links to its source — and every method here is what we run for real client campaigns.