Is GEO only in our heads, or something real?

I was deep in thought about how much of a model hallucination is RLHF-based assumption in the absence of training data or context. Pretraining gives the model the ability to confabulate. Post-training often influences whether it chooses to confabulate rather than say "I don't know."

A base language model is trained to predict plausible continuations. If the evidence needed to answer is absent from its weights or context, there's no fundamental mechanism in next-token prediction that says "stop, evidence unavailable." It still has a probability distribution over tokens, so it generates the most plausible continuation. Hallucinations therefore exist without RLHF.

TruthfulQA demonstrated this with pre-RLHF base models such as GPT-3, GPT-2 and GPT-Neo/J: they confidently reproduced misconceptions learned from text, and the larger models were less truthful, not more.

But this is a slightly narrow point of view for web people like us. For us, when we think of hallucination we think of a model telling an untruth. Which the above is really a subset of.

Seen in the broader sense, hallucination decomposes like this:

1. Wrong information encoded during pretraining. The model learned a false claim because the corpus contained it, or because statistical compression distorted the underlying facts.
2. Imperfect retrieval from model weights. The correct information is effectively "in there," but generation retrieves the wrong association: wrong year, wrong person, conflated events.
3. Inference errors. The premises are available, the model reasons incorrectly.
4. Confabulation under missing information. The model lacks the fact and constructs the most plausible answer anyway.
5. Post-training induced behaviour. SFT, RLHF and preference tuning make 1 to 4 more or less likely to be verbalised as an assertion, depending on whether the learned behaviour rewards answering, agreeing, sounding decisive, abstaining or checking uncertainty.

I know what you're thinking, this is still narrow. We don't interact with bare models like this. We interact with fully fledged ecosystems with so many moving parts: model, system prompt, memory, retrieval, tools, planners, execution loop, verifiers, reward shaping, UI constraints, external state. What fancy people call harnesses.

In that environment an untrue response can arrive for a much wider variety of reasons:

• The base model invents a fact.
• Retrieval returns the wrong document, stale information, or a semantically similar but incorrect result.
• The model misreads correct retrieved evidence.
• Tool selection is wrong: it searches the web when it should query an MCP server, queries the wrong database, or fails to use a tool at all.
• The planner makes an unsupported assumption early, and every later step treats it as established fact.
• Memory injects an incorrect prior fact.
• A tool succeeds partially, but the agent interprets partial output as complete.
• The agent loses provenance across steps and can no longer distinguish observed facts from inferred facts.
• A verifier checks linguistic consistency rather than correspondence with reality.
• The system prompt or reward function pressures the agent to "finish the task," so it bridges missing state with assumptions.
• One agent invents something and another agent treats that output as evidence.
• The final response generator turns an uncertain intermediate state into a declarative statement.

Why even think about any of this? Well, because if you're to believe GEO is a real discipline, you have to believe you can impact some of these factors in a controlled way as an outsider. Otherwise there'll be nothing to optimise, right?

The important distinction is control over the model versus control over the evidence environment the model operates in.

What can we do then?

Leverage is qualified the same way throughout: where a remediation requires you to operate a machine endpoint, the leverage is conditional on your doing so. Rows 4, 7 and 8 all carry that condition, and an earlier version of this table applied it only to row 4.

2, 3, 4, 5, 7 and 8. Those are the genuinely externally addressable hallucination surfaces. And this is much bigger than conventional GEO.

What follows adds something the argument didn't have when I first wrote it: measurements of how those six surfaces actually look across the web, rather than only arguments that they matter.

Wrong or stale retrieval is probably the biggest opportunity

RAG exists precisely because supplying current external information reduces dependence on faulty parametric knowledge. Models frequently update an incorrect parametric belief when given correct external evidence, although strong priors can still cause resistance. Retrieval quality matters enormously: wrong, irrelevant and conflicting documents measurably degrade answer correctness.

A business can directly work on this. Suppose models incorrectly say "Lumar doesn't support JavaScript rendering." You want a canonical URL that states the fact plainly:

Does Lumar crawl JavaScript websites?
Yes. Lumar renders pages using Chromium and crawls content generated by client-side JavaScript.

rather than expecting the model to derive it from 1,800 words of marketing copy. Then make sure that page is crawlable, internally linked, canonical, indexed, current, server-rendered or otherwise trivially extractable, and not contradicted by three old documentation pages.

And here's the part I can now measure rather than just assert (Agentic readiness of the web, corpus scan). The retrieval substrate isn't in good shape. Of the 88,484 publishing hosts, 61,587 publish an XML sitemap and 21,448 of them break the format, about 35%. The single most common fault anywhere in the study is a sitemap sitting in a subfolder that lists URLs outside it: 14,176 sites, 23% of everyone with a sitemap. Google reads those URLs anyway if you submitted the file through Search Console, which is why so many teams have never seen it break. Hand the same file to anything that isn't Google and the out-of-scope entries are dropped.

The sitemap protocol was agreed in 2005. If the oldest, most boring piece of retrieval plumbing on the web is broken on a third of sites that bother to publish it, the idea that the evidence environment is already optimised and only model behaviour is left to tune doesn't survive contact with the data.

Basic freshness engineering matters too: Google [explicitly uses trustworthy lastmod values (https://developers.google.com/search/blog/2023/06/sitemaps-lastmod-ping) as a recrawling signal.

One correction worth making precisely, in an article about precision. For ChatGPT Search, OpenAI says that "sites that are opted out of OAI-SearchBot will not be shown in ChatGPT search answers, though can still appear as navigational links," and that they "recommend allowing OAI-SearchBot." That's narrower than "must be allowed to be eligible": you lose the answers, not the link. They separately state GPTBot "is used to crawl content that may be used in training our generative AI foundation models," which is the distinction that matters here.

It's also a decision very few sites have taken deliberately (corpus scan). 33,626 sites name at least one AI crawler in robots.txt, and only 4,289 of them name OAI-SearchBot at all, about one in eight. A caveat I won't skip:
naming a crawler isn't blocking it, and a crawl can't see which way a rule points. What the number shows is how few sites have made any explicit decision about the one bot that governs appearing in ChatGPT answers.

None of this is speculative "LLM SEO." It's improving the evidence retrieval systems have available.

Correct evidence that the model misinterprets is also addressable

This is where I'd invest substantially. Models do worse as irrelevant context increases, and evidence placement affects factual verification.

So stop asking "is this information somewhere on our site?" and start measuring:

Can a machine retrieve the smallest sufficient passage that answers the question without inference?

Those are very different standards.

Bad:

Our platform empowers modern enterprise teams through an advanced rendering infrastructure designed to help
organizations understand experiences across increasingly dynamic digital environments...

Good:

Does Lumar crawl JavaScript websites?
Yes. Lumar can render pages using Chromium and crawl content generated by client-side JavaScript.

This matters because you're reducing the amount of reasoning between evidence and claim. I'd call the metric something like inference distance: how many unsupported transformations are required to get from the source material to the desired factual answer. You want that close to zero.

Tool selection is increasingly something businesses can influence

This is where the agentic ecosystem gets interesting, and where I have the sharpest numbers.

MCP and tool-selection research shows measurable differences in models' susceptibility to misleading tool descriptions and overlapping capabilities. A 2026 study ran 8,640 agent runs and found substantial variation in wrong-tool susceptibility depending on tool design and model capability.

So if you expose:

{ "name": "query", "description": "Query the system" }

versus:

{
  "name": "salesforce_get_account_by_id",
  "description": "Authoritative source for Salesforce CRM account records. Returns the current
                  account owner, tier and renewal date for one account ID. Not for opportunities,
                  contacts or historical snapshots."
}

the latter makes epistemically correct tool selection much easier. You can't force an agent to pick your tool. But you can remove ambiguity from the decision.

And this is where the numbers get embarrassing (corpus scan). Across the 88,484 publishing hosts:

Look at row three again. 112 of 497 machine-readable API descriptions give an agent nothing stable to name a call by (corpus scan). That's not a theoretical concern about tool ontology. It's the concrete failure this section argues about, already present in 23% of the sites that have gone to the trouble of publishing a contract at all. And 40 server cards advertise an MCP server without saying where it is.

For businesses this suggests a new optimisation surface:

Don't merely expose an MCP server. Engineer its ontology so an agent can reliably determine when it is the > authoritative source.

Unsupported planner assumptions are surprisingly addressable

Consider: "Why is Lumar more expensive than Screaming Frog?"

If neither company has published meaningful evidence, an agent will generate something plausible. Possibly complete fiction. A company can anticipate those inference gaps and publish the answer:

How does Lumar's pricing compare with Screaming Frog's?
They are priced for different jobs. Screaming Frog licenses a desktop crawler per seat. Lumar is billed on > crawl volume and retained history across a whole estate. A single-site audit is cheaper in Screaming Frog; > continuous monitoring of many domains is what Lumar is priced for.

Or, importantly, publish the negative:

Does Lumar offer a free tier?
No. Lumar does not offer a free or perpetually free plan.

Negative knowledge is particularly useful. Companies publish enormous amounts of X does Y and very little X does **not* do Y*. False assumptions live precisely in that missing space.

Provenance may be the biggest long-term opportunity

Generative search research has repeatedly found provenance problems. Liu, Zhang and Liang's Evaluating Verifiability in Generative Search Engines
(Findings of EMNLP 2023) audited four engines by human evaluation and found only 51.5% of generated sentences fully supported by their citations, and only 74.5% of citations actually supporting the sentence they were attached to.

That study is worth citing carefully rather than waving at. It is from 2023, and it audited Bing Chat, NeevaAI, perplexity.ai and YouChat. NeevaAI no longer exists. It establishes that the problem is real and was severe; it doesn't describe 2026 systems, and I haven't found an equivalently rigorous replication since.

For a business-controlled machine endpoint, I'd therefore return facts more like:

{
  "claim": "Lumar renders JavaScript using Chromium",
  "value": true,
  "source": "https://www.lumar.io/docs/crawler/javascript-rendering/",
  "asOf": "2026-09-01",
  "basis": "product documentation",
  "supersedes": null
}

Not because the LLM necessarily needs every property. Because every downstream agent now has the opportunity to preserve provenance. That attacks #8 directly and helps #5, #9, #11 and #12 indirectly.

And the gap here is close to total (corpus scan). 73 sites publish an A2A agent card and 15 of them are valid; 58 are missing fields the card is required to carry. Of the 191 sites publishing MCP authorization metadata, 121 are valid. The machinery for machine-readable, attributable answers exists, it's barely deployed, and where it is deployed it's usually wrong.

Where I'd be much more skeptical

Base model correction

This is where a lot of GEO marketing is BS. There's no good evidence that adding llms.txt, rewriting a page a certain way, adding some schema, or mentioning a fact five times reliably causes frontier model parametric knowledge to change.

A company doesn't control when the next model trains, its corpus, deduplication, weighting, filtering, synthetic-data pipeline or post-training. Allowing GPTBot means content may be used in training. It absolutely does not mean a factual correction will be absorbed.

Worth noting what the most-adopted intervention actually is (corpus scan): 13,730 sites publish llms.txt, making it the most widely adopted agent-facing file on the web after robots.txt and the sitemap. Ahrefs found 97% of the llms.txt files they tracked receive zero AI requests. The most popular lever is the one with the least
demonstrated demand behind it.

And current GEO evidence supports that skepticism

There's now quite a lot of GEO research. Controlled studies show altering page structure and content can increase citation visibility. But a review of 45 GEO studies concluded that the strongest evidence concerns what happens once content is already available to the generative system, and found no demonstrated stable, longitudinal, cross-platform causal method for improving organic discoverability.

That's an enormous distinction. That's how I'd communicate it to customers.

So I think there is a much better business category hiding here

Not Generative Engine Optimization, and not make AI mention your brand more. Rather:

AI factual integrity for organizations.

The product asks models hundreds of questions about the organization. For every claim it identifies, it classifies the failure mode against the twelve above. And crucially it doesn't merely give a brand score. It says this error is addressable, and here is why:

"Three of four models say Lumar cannot crawl JavaScript. The claim is wrong. There is no canonical page > stating it plainly; the fact appears once, in the eleventh paragraph of a product page, alongside two > older documentation pages that contradict it. This is failure mode 2 and 3. Publish a canonical answer > page, retire the contradictions, and re-measure."

That is empirically testable. After the change, rerun the same experiment. Not once, because these systems are stochastic, but enough times to separate a real shift from sampling noise.

Now you're not selling snake-oil “AI visibility.” You are running something much closer to observability and remediation for how agents perceive an organization.

Diagnose all twelve. Sell remediation aggressively for 2, 3, 4, 5, 7 and 8, where the organization genuinely has meaningful control. For 1, 6, 9, 10, 11 and 12, report the problem and distinguish what can be mitigated from what belongs to the model or agent provider.

That distinction may be the strongest thing about the product, because it avoids pretending every wrong answer can be fixed with another llms.txt file.

And it has a precedent in how we already report. In the same corpus scan, the one security-impact check the scanner carries, whether a site has published private key material in its public signing-key directory, fired zero times across 59 publishers (corpus scan). The honest way to report that isn't "the web is secure." It is: nobody in this sample did the dangerous thing, the sample is 59, and by the rule of three that's consistent with a true rate anywhere up to about 5%. A product that reports a null result that carefully is a product whose positive findings you can believe.