When LLMs Go ‘Poof’: Understanding Grounding, Hallucinations, and SEO

What Happens When LLMs Can’t Find Their Anchors — and Why SEO Still Matters

I had the pleasure of attending the Tech SEO Connect conference in Durham, NC over the last couple of days, and I must say, my cup is full :)

Several of the presentations honed in on how I’ve been thinking about how large language models (LLMs) behave, and why SEO infrastructure matters more than ever.

Some stand out highlights of this conference:

1. affordability — a low key–warehouse style venue, there were plenty of food and drinks, baristas providing custom coffees (!!), and lots of great local eateries.
2. fantastic expert lineup — longtime favorites including: Cindy Krum, Dana DiTomaso, Jamie Indigo, and Rachel Anderson, plus lots of newer-to-me fascinating, expert speakers
3. every presentation was a winner — so many actionable takeaways
4. single track topic — no stress about missing sessions
5. networking — lots of opportunities between presentations and at after hours events
6. there were puppies

‘Grounding’ & ‘Fan Out’

I’m going to drill down on two concepts that came up repeatedly and that do a good job of capturing ideas I’ve been thinking about for a while: grounding and query fan out.

When I talk about LLMs finding their anchors, this is what I mean.

• Grounding – per Drishti Shah: “LLM grounding refers to the process of linking AI-generated responses to factual, authoritative sources.”

  In other words, grounding is how a model anchors its output to concrete, trustworthy information.

  This is a concept that I’ve been thinking about a lot, LLMs needing concrete and credible information in order to present reliable feedback.

  LLM knowledge can feel somewhat ethereal — when their reasoning is anchored by structured data and consistent entity references across sources, they can be a solid source of information. But take away those anchors, and the grounded part of their reasoning goes poof.

• Fan Out — often referred to as “query fan out”, describes the retrieval process an LLM uses when responding to a prompt; the process of an LLM taking the language of the user’s input and simultaneously checking for references to the different parts of the user’s meaning and intent across the resources it can access at response time. It retrieves the most relevant information, and refines its response by comparing and contrasting those signals.

  Put more simply:
  1. Fan out is about how widely and intelligently the model searches for relevant signals.
  2. While grounding is about how well those signals are anchored to concrete, trustworthy facts.

  (The term ‘fan out’ was mentioned by more than one presenter. Michael King gave a presentation on Query Fan Out).

In essence, the more grounded (concrete) the information an LLM can retrieve in relation to the user’s prompt, the more reliable — and less hallucinatory — its response will be.

Why Hallucinations Happen

Hallucinations occur when a model is forced to predict rather than retrieve—when it doesn’t have the necessary information to produce a concrete answer. In cases like these, it does what it was trained to do: it predicts the most plausible continuation, even if that means confidently making something up.

Hallucinations can happen either because relevant information is missing, or because the retrieved signals are weak or inconsistent.

Why don’t LLMs just say “I can’t reliably answer that”

This behavior isn’t intentional; it’s a direct consequence of how these systems are built and trained.

Two key reasons are:

1. The architecture is generative, not discriminative – LLMs are trained to predict the next token that best continues the conversation based on patterns in their training data. (Brown et al., 2020¹). In simple terms they are like a massive autocomplete engine.
2. Training data contains millions of examples where humans “confidently guess” (Bender & Gebru et al., 2021²; Lin et al., 2021³). In books, articles, and forum conversations, people rarely say: “I don’t know enough to answer that.”, they more often offer a confident-sounding explanation — even when incomplete or speculative — LLMs learn this pattern.

Website Optimization

This is where all of this ties directly back to SEO and AIO. Make sure your foundation is solid using traditional website optimization best practices — maintain clarity and consistency in:

• urls/directory structure
• title tags
• meta descriptions
• heading hierarchy
• internal linking + anchor text
• Schema.org structured data

… these have always helped search engines understand content. Historically these are important tagged “snippets” that search engines value and potentially trust.

As search and conversational systems increasingly share retrieval infrastructure, these same signals now help with both grounding and fan out. When your URLs, headings, internal links, and structured data clearly point to the same topics and entities, a model’s fan-out process is more likely to retrieve multiple highly relevant, mutually reinforcing signals—from your site and from external sources.

This is similar to how local business citations work. When the same words (and numbers) related to your business (think NAP: Name, Address, Phone number) are consistently repeated across multiple sources, the data, and in turn your business, is viewed as more credible and trustworthy.

Final Takeaway

If you want your business, your products & services, and your content, surfaced to potential customers in search and conversation engines, it needs to be concrete, repeatedly sourced, and reliable. Then when models fan out, they consistently find you, and when they ground their answers, they can safely lean on your content.

And if you want to up the bar on attendee happiness at your next conference, definitely provide puppies!

---

Learn more about Tech SEO Connect here:

• Tech SEO Connect 2025 website: https://www.techseoconnect.com/
• Presentations on YouTube: https://www.youtube.com/@techseoconnect/

Related Research:

1. Brown, T. et al. (2020). Language Models are Few-Shot Learners. https://arxiv.org/abs/2005.14165
   — Foundational paper explaining the generative next-token architecture.
2. Bender, E. M., Gebru, T. et al. (2021). On the Dangers of Stochastic Parrots. https://dl.acm.org/doi/10.1145/3442188.3445922
   — Shows how LLMs learn human linguistic patterns, including confident guessing.
3. Lin, S. et al. (2021). TruthfulQA: Measuring How Models Mimic Human Falsehoods. https://arxiv.org/abs/2109.07958
   — Demonstrates how models imitate confidently expressed human errors.