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How LLM Optimization is Different from SEO

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The Widening Gap: Why Most Websites Aren’t AI-Ready

I get this question a lot — and it’s a good one.

Traditional SEO focused on ranking. But with generative AI, visibility means showing up in the answers — not just the search results.

SEO focused on optimizing for search engines: keywords, metadata, backlinks, page structure, and page speed.

But here’s the thing: most company websites aren’t great at answering questions.

And that gap is widening – here’s why…
Line chart comparing “Answer Readiness” of LLMs vs. brand websites from Q3-2022 to Q4-2025. LLMs show a steady upward trend, while brand sites remain flat. Annotations highlight LLM traits like “24/7,” “Conversational,” and “Personalized,” versus brand site traits like “Static,” “Too Salesy,” and “High Friction.

LLMs like ChatGPT are rapidly increasing their ability to deliver helpful answers — while most brand websites remain static and unprepared.

While LLMs continue to improve their “answer readiness”, the average brand site has barely moved. That’s a missed opportunity — and it’s exactly what content teams need to fix. Get AI-Ready

But now we’re dealing with a new layer: AI tools like ChatGPT, Gemini, Claude and Copilot aren’t just pointing people to links — they’re answering questions directly.

So it’s no longer just about ranking in Google.

It’s about being *included* in the answer.

A link? Great if you get one. Whether a link to your content shows up depends on the query, the platform, and how the AI decides to generate the answer.

What you can control is how easy it is for your content to be found, quoted, and summarized by AI.

LLMs Don’t Crawl Like Search Engines

They don’t crawl or index live web pages in real time like search engines do.

Instead, they’re trained on massive datasets built from publicly available or licensed content — including Wikipedia, product listings, documentation, and structured data feeds.

Why? Because LLMs learn from chunks of content (called tokens), not full web pages.

They’re trained on snippets like headlines, summaries, FAQs, and structured content—not long, unstructured pages.

Some AI Tools Retrieve, Not Just Train

Not all AI tools rely only on training data. Some — like Google’s Search Generative Experience (SGE), Microsoft Copilot, and certain versions of ChatGPT and Claude with browsing — can retrieve fresh information from the web at runtime.

That means they aren’t just responding based on what they were trained on months ago — they’re also pulling from live sources like public websites, news articles, help docs, and product pages.

But even then, these tools don’t crawl the internet like a search engine. They rely on pre-indexed or structured content — and they prefer sources that are clear, scannable, and trustworthy.

In other words, if your content is hard to parse, buried in fluff, or scattered across PDFs and long pages, it’s less likely to be surfaced — even in real-time tools.

At ToTheWeb, we use Revere to monitor how brands show up in LLMs — from citations in chatbot responses to rankings in AI-generated answer summaries. It’s been an incredibly useful way to track visibility across platforms like ChatGPT, Gemini, and Claude.

AI Brand Management Score

This snapshot from Revere represents a small but valuable example of the types of brand insights that AI-powered brand management platforms can generate — including sentiment scores, brand perception summaries, and competitive positioning data.

AI Brand Management Scoring

What’s the best way to show up? Make your content clean, structured, and easy for machines to understand.

That’s why well-organized content often gets surfaced, while long, unstructured pages get overlooked.

Here’s the upside: many traditional SEO best practices still apply.
Use clear headers.
Write in short paragraphs.
Keep a logical structure.

All of that still improves visibility — just for a different kind of system. In practice, LLMs generally absorb content found in:

  • Wikipedia entries
  • Blog post intros
  • News story summaries
  • Product feeds
  • FAQs and comparison tables
  • Webinar transcripts and recap posts

Large Language Models favor content that’s easy to scan — not buried in dense walls of copy.

Why might that be??  Simple – there’s no structure to a block of text.

When I first started optimizing for LLMs, this insight changed everything about how we formatted client content.

We saw this firsthand with a client.

We rewrote a 2,000-word article using LLM optimization best practices — tighter structure, clearer takeaways, improved headings and subheadings, a more conversational tone, and a clean, structured schema.

Within weeks, it was showing up in Google’s AI-generated summaries — complete with a direct link to the article.

“Across the board, we’re seeing more web traffic from LLMs like ChatGPT, and early data shows this traffic converts well, so clients are eager to get more of it… To fhelp clients appear in LLM results more often, we’re guiding them toward this conversational, question-and-answer-focused writing style. For brands in complex industries, this is a big shift, so we work together to create content that maintains their brand voice and communicates technical ideas in ways that resonate with both buyers and LLMs.”

The SEO BloodbathCarey Madsen, Vice President, The Fletcher Group
– PR and content marketing firm focused on fintech and payments

Authority Looks Different Now

In traditional SEO, authority came from backlinks, domain age, and ranking signals.

You built it over time — mostly through your website.

But with generative AI, the game has shifted.  I’ve watched this transformation happen over the past year, and it’s fascinating how quickly the metrics of influence have changed

What matters now is “entity authority” — whether your brand shows up consistently across trusted sources online.

I’ve seen companies pour resources into their websites while ignoring everything else.

But AI learns from all of it: product listings, news mentions, schema markup, customer reviews.

Take one mid-sized brand we worked with.

They improved their visibility in AI-generated answers by leaning into strategic PR — not more blog posts.

They published a series of thought leadership pieces, partner announcements, and product launches — all picked up by outlets like Business Wire, TechCrunch, and niche industry sites.

Because that content was well-structured, widely syndicated, and published on authoritative domains, it gave the AI multiple touchpoints to associate the brand with key topics.

A few weeks later, their name started showing up in chatbot summaries and SGE results — not just from their own site, but from quotes pulled directly from third-party coverage.

It’s no longer just about your website.

It’s about your entire digital footprint — everywhere your brand can be seen, named, structured, and understood.

Think Like a User, Not an Algorithm

LLMs don’t care about keyword stuffing.

They’re trained to understand what people are actually asking.

Content that answers real questions — in natural language — performs better.

Instead of writing “POS software,” ask:
“What’s the best POS system for a small retail store?”

That’s how users think.

And that’s how AI models evaluate relevance.
I’ve done the over-optimization thing in the early SEO days but this is better — and it feels better to write.

LLMs interpret intent – not just keywords

So optimize for clarity, usefulness, and the way people actually phrase their questions.

Also worth noting: Google recently updated its guidance for human content raters. While raters have been asked to evaluate AI-written content, they weren’t given tools to detect it.

It’s not about who wrote the content — it’s about whether it’s useful.

Not Showing Up in AI Answers?

Don’t outsource what your team can master.
Train them to write for AI with us.

Master AI Content

You’re Now Optimizing for More Than Just Search

It’s not just about Google anymore.

Your content is now being used in AI-powered tools that serve answers directly to users, including:

  • Public-facing chatbots
  • Voice assistants
  • Generative search experiences like Google’s SGE
  • In-product assistants like Microsoft Copilot and Google Gemini

Each of these tools surfaces content differently.

Chatbots want clear product specs.

Voice tools need short, conversational answers.

AI search favors comparison tables and structured FAQs.

Copilot and Gemini work best with well-structured, scannable guides — especially when they pull from public knowledge sources.

Note: These tools don’t access private content. They surface what’s public — blog posts, FAQs, reviews, and structured data.

To show up, your content needs to be built for more than search engines. It needs to be AI-ready — wherever users ask, not just where they search.

Take Action: Be AI-Ready

The shift from traditional SEO to AI-ready content isn’t coming—it’s already here.Companies adapting their content for AI visibility are gaining ground, while those clinging to outdated SEO tactics are increasingly missing from the conversation.

Ready to move forward? Explore our AI Search Optimization Guide and GEO Checklist, or contact us to train your content team on AI-ready writing.

The question isn’t whether AI will change how people find your business—it’s whether you’ll be ready when they ask the questions only your brand can answer.

Frequently Asked Questions

Yes, but it requires a different approach than SEO tracking. Tools like Revere.ai can monitor citations across platforms like ChatGPT, Gemini, and SGE. You can also manually test prompts, analyze AI summaries, and track referral patterns. Visibility in LLMs is still an emerging metric — but it’s measurable and actionable.

We use Revere's product:  Luminaire  to monitor brand presence and we use Google Analytics to measure inbound traffic and conversions from LLM models like ChatGPT, Gemini, Perplexity, Claude, etc.

 

You may stop being part of the conversation. AI-generated answers are becoming the first touchpoint for product research, vendor comparisons, and customer decision-making. If your brand isn’t included, your competitors will be. The longer you wait to optimize, the harder it becomes to reclaim visibility in AI ecosystems already training on others.

LLMs prefer structured, scannable, and specific content. This includes:

  • FAQ sections
  • Summary paragraphs
  • Product specs and tables
  • Schema markup
  • Comparison charts
  • Thoughtfully formatted blog post intros
  • Clear headings and subheadings
  • They tend to favor content that mirrors the way real people ask questions — not jargon-heavy or overly long pages.

LLMs like ChatGPT are trained on curated datasets — often compiled months before you use the tool. Others, like Google’s SGE or Microsoft Copilot, retrieve content from pre-indexed sources at runtime.

But even these retrieval-based tools rely heavily on structured, public-facing data. They don’t “crawl” in real time. They prefer well-formatted content that’s easy to summarize, quote, or cite — and they often ignore long, messy pages or PDF files.

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