Where Do LLMs Get Their Data? a Guide for Marketers

You rank well on Google. Your category pages are polished. Your brand team has the messaging dialed in.

Then someone asks ChatGPT for the best tools in your space, and your competitor shows up instead of you.

This is why marketers keep asking where do LLMs get their data. If AI systems build answers from certain kinds of sources, and your content doesn't fit that pattern, you can disappear from the recommendation even when your SEO looks healthy.

So the primary issue to focus on in your SEO is whether your content looks useful enough, structured enough, and clear enough to align with what the models are indexing.

Why Your AI Visibility Depends on LLM Data

If your team cares about AI visibility, you have to care about training data.

That's because AI answers don't come out of nowhere. Platforms like ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews generate responses based on patterns they learned from large training corpora and later tuning. When they recommend one vendor over another, there's usually a content reason behind it.

Why rankings and recommendations drift apart

A page can rank in Google and still fail to influence AI answers. I see this most often with B2B sites that lean hard on product marketing copy. The page sounds polished, but it doesn't answer comparison questions cleanly. It doesn't define terms. It doesn't offer structured facts, tradeoffs, or FAQs.

AI systems tend to do better with content that looks easy to extract and recombine.

Practical rule: If a buyer asks, "Which tool is best for this use case?" your site needs at least one page that answers that question directly in plain language.

That's the shift. Traditional SEO tries to win a click. AI visibility tries to become part of the answer.

What marketers usually miss

Many organizations ask, "How do we rank for this keyword?" Fewer ask, "What kind of source would an LLM treat as useful training material or citation material?"

That second question changes your content strategy.

A homepage rarely does the heavy lifting here. A well-structured comparison page might. A detailed FAQ might. A practical guide that defines category terms, buyer criteria, and tradeoffs often has a better shot than a generic thought leadership post.

Here's the simple version:

• Broad web presence matters because LLMs learn from public internet text.
• Specialized content matters more when a model needs a reliable answer about a category, product type, or buyer decision.
• Structure matters a lot because messy pages are harder for systems to use.

When marketers understand the data pipeline, they stop treating AI visibility like magic. They start treating it like content fit.

The Digital Library LLMs Read

The easiest way to think about an LLM is as a student studying from a giant digital library.

That library is massive, but it isn't one shelf. It has sections. Some teach broad world knowledge. Some teach formal writing. Others teach logic, code, technical concepts, and conversational style.

To make that concrete, here's a visual model.

The biggest shelf is the public web

For the foundational GPT-2 model, 60% of training tokens came from a filtered version of Common Crawl, 22% from pages linked from popular Reddit posts called WebText2, and 16% from two book corpora, according to this breakdown of GPT-2 training sources.

That mix matters. It tells us the web is the main raw material, but not the only one. It also shows that conversational sources and books play a big role in how the model learns tone, structure, and explanation style.

A broader dataset composition described in the same source shows Common Crawl at approximately 67%, followed by C4 at 15%, then smaller but meaningful portions from GitHub at 4.5%, Wikipedia at 4.5%, Project Gutenberg at 4.5%, ArXiv at 2.5%, and StackExchange at 2%. That's a useful reminder that LLMs don't train on a single "internet dump." They train on a curated mix of general web text and narrower high-signal sources.

Different shelves teach different skills

A simple way to map the library looks like this:

That's why standard product pages often underperform in AI answers. They're usually written to persuade, not to teach.

The content most likely to influence AI answers often reads more like documentation, comparison research, or a buyer guide than a campaign page.

Why marketers should care about formatting

The library analogy also explains why formatting matters. A page with a strong heading structure, scannable sections, and direct answers is easier to absorb than a page built from slogans and vague claims.

If your team is building pages meant to be machine-readable as well as human-readable, tools like this SEO tool for LLMs can help you create cleaner input formats from existing content. That doesn't guarantee visibility, but it does push your content closer to the kind of material AI systems can interpret well.

How Raw Data Becomes Refined Answers

A model can read from a huge digital library and still give weak answers if the material is messy, repetitive, or unsafe to learn from.

LLMs do not absorb the internet in one pass. They go through a pipeline that looks more like a sorting facility and an editorial process combined. Data is collected from many sources, cleaned, filtered, broken into training-ready pieces, and only then used to teach the model patterns in language. After pre-training, human feedback often shapes which responses the model should prefer.

First, the system turns the web into usable training material

Raw text from the web is noisy. The same article may appear in multiple places. Some pages are machine-generated junk. Some contain personal information that should not be retained. Some are so thin that they teach very little.

So the first job is reduction.

The pre-training pipeline usually includes filtering by language, removing near-duplicates, excluding low-value or unsafe sources, normalizing formats, and splitting text into smaller units the model can train on. Hugging Face's explanation of how pretraining data is prepared for language models gives a useful overview of this process.

For marketers, visibility often rises or falls at this point. A polished brand page can still be poor training material if it says very little, repeats the same positioning language, or hides the useful explanation under banners, tabs, and generic copy.

A good way to frame it is ore versus metal. Raw web content is the ore. Training data is the refined material after the waste has been removed.

Then the model learns patterns, not facts in a spreadsheet

Once the dataset is cleaned, the model trains on token sequences and learns statistical relationships across that corpus. It is not storing neat rows called "best CRM vendors" or "top payroll tools." It is learning how words, claims, comparisons, explanations, and answer structures tend to appear together.

That point matters because many marketing teams assume publication alone creates AI visibility.

It does not. Pages help the model more when they contain reusable patterns such as definitions, comparison criteria, product categories, use cases, objections, and direct answers to real questions. If your team is working on answer engine optimization strategies for AI search visibility, this is the practical reason those methods matter. You are making your content easier for training and retrieval systems to use.

Then humans shape response preferences

Many models also go through fine-tuning after pre-training. One common method is Reinforcement Learning from Human Feedback, or RLHF. In that process, human reviewers compare outputs, rate which answer is more helpful or safer, and those preferences help train the model toward better response behavior, as described in this RLHF explainer.

That is why two answers built from similar background knowledge can sound very different in practice. One is vague and rambling. The other is structured, cautious, and easier to trust.

Your content can benefit from that preference layer if it already mirrors the traits humans reward: clarity, directness, context, and useful structure.

What this means for your brand

Here is the practical version.

If your page is hard to parse, thin on substance, or full of recycled claims, it has two chances to lose. It may be filtered out before training, or it may provide weak patterns that are less likely to surface in later AI responses.

If your page explains a topic cleanly, uses descriptive headings, answers specific questions, and adds original context, it is more likely to become useful model input.

That is why marketers should treat content production less like ad copy development and more like preparing source material for an analyst. Teams working with messy public datasets already use this mindset. The guide on cleaning social data for ML engineers is a good example of the kind of preparation logic that improves what a model can learn from in the first place.

The Link Between Data Sources and AI Citations

The topic stops being abstract at this point.

Marketers don't usually care about Common Crawl for its own sake. They care because some brands appear in AI answers and others don't. The missing piece is the gap between being online and being usable by AI systems.

The citation gap is real

LLMs prioritize structured, high-authority comparison content over standard marketing pages, which leaves many B2B brands invisible in AI answers. This citation gap exists because models may discard up to 80% of scraped web content due to spam, duplication, or low-quality filtering before training starts, based on this discussion of the citation gap in AI search.

That single idea explains a lot of frustrating outcomes.

You may have a nice category page, but if it mostly repeats positioning statements, the model has little to work with. A competitor with a page titled "Intercom Alternatives for SaaS Support Teams" that includes buyer criteria, side-by-side comparisons, and FAQs may become much more useful as an AI input.

What gets picked up more easily

I'd separate content into two buckets.

Content AI systems struggle to use well

• Generic product copy that talks in slogans
• Thin blog posts built around broad keywords
• Feature pages with no context, definitions, or comparisons

Content AI systems can use more easily

• Comparison pages with clear differences
• FAQ hubs written in plain language
• Buyer guides that answer category questions directly
• Technical explainers with specific terminology and definitions

That's a big part of answer engine optimization. You're not trying to game a crawler. You're making your content more usable for systems that synthesize answers.

One useful test: If a sales rep could send the page to a buyer who asks a comparison question, it's probably closer to citation-worthy than a page built only for brand messaging.

Clean source material beats fancy design

A lot of teams invest in visual polish and still miss the substance. AI systems care more about extractable information than design flair.

If you're turning messy pages, PDFs, or exported docs into cleaner machine-readable assets, a tool that helps generate clean data for LLMs can make repurposing easier. The true win, though, comes from writing the original content in a cleaner form from the start.

Common Misconceptions About LLM Data

The topic gets muddy fast because people mix up training, retrieval, and storage.

I hear the same two assumptions all the time. First, that LLMs store your website like a searchable filing cabinet. Second, that if you don't like how a model mentions your brand, you can ask for removal the way you'd update a database record.

LLMs don't keep pages the way a CMS does

LLMs do not store personal data in a retrievable format after training. They learn statistical associations instead. Because of that, post-training data removal is technically infeasible without completely retraining the model, as explained in this analysis of data in pre-trained LLMs.

That distinction matters for brand teams.

If your product page existed in training data, the model doesn't hold a neat copy of that page ready to fetch later. It learned patterns from the text. That's why answers can resemble source material without acting like exact database lookups.

You can't just "submit" your brand and expect inclusion

Many marketers get stuck at this point. They assume there must be a form, feed, or direct route into the model.

There usually isn't.

A model can be updated through later methods like fine-tuning or instruction tuning, but your average marketing page doesn't get manually inserted into a frontier model. That's also why reactive brand control is limited. Once training is done, changing what the model "knows" is much harder than publishing a new page on your site.

Here's the practical takeaway:

• You can't rely on removal requests to clean up old model knowledge.
• You can't rely on rankings alone to earn AI mentions.
• You should publish useful, structured content early so the right signals exist before models and answer systems process them.

If your team treats AI visibility as something you'll fix later, you're already behind. This is a publishing and content design problem first.

Your Checklist for Creating Citation-Worthy Content

What makes one page easy for AI systems to cite while another page gets ignored?

Marketers often assume the winner is the site with the most content. In practice, AI systems tend to favor content that is easier to parse, easier to trust, and easier to reuse in an answer. If the earlier sections described the library LLMs read, this section is about how to make your page worth pulling off the shelf.

Here's a practical checklist I'd use with a SaaS marketing team that wants stronger AI visibility, not just more indexed pages.

Start with buyer prompts

Topic buckets are too vague. Buyer prompts are specific.

A content theme like "customer support software" can produce a broad, forgettable article. A prompt like "best live chat software for SaaS companies" creates a clearer job for the page. It tells you what the reader is trying to decide, what comparisons they need, and what language an AI assistant is likely to reuse.

Useful examples include:

• Best live chat software for SaaS companies
• Top alternatives to Intercom
• Best AI SEO tools
• How do I track my brand in ChatGPT

This is the shift that matters for brand visibility. You are not only writing for a human visitor. You are also publishing answer-shaped material that AI systems can quote, summarize, or cite.

Build pages that do more than one job

Strong citation candidates rarely stop at a definition or a sales pitch. They work like a good analyst brief. They answer the main question, explain key terms, compare options, and help the reader make sense of tradeoffs.

That usually means adding elements like:

• Comparison tables that separate features, fit, and limitations
• FAQ sections with direct, plain-language answers
• Use case blocks for different buyer types or team sizes
• Glossary-style explanations for terms people often mix up

If your page only says "we are the best solution," it gives an AI assistant very little to work with. If your page explains categories, criteria, and differences, it becomes much more reusable.

Fix structure before adding volume

Many brands already have useful information. It is just trapped inside pages that are hard to scan.

AI systems process text a bit like a busy researcher reviewing documents. Clear headings, short sections, tables, labeled lists, and direct answers reduce the work required to interpret the page. Messy structure creates friction. Clean structure helps both readers and answer engines find the part that matters.

If your team wants a broader framework for that work, this guide to AI search optimization gives a useful next step.

Publish original insight where your team already has expertise

You do not need a large research program to create material worth citing.

Your company probably already has original knowledge in sales calls, onboarding docs, support macros, implementation notes, and product comparisons. Those repeated explanations are often your best raw material. A useful rule is simple. If your team answers the same question every week, turn that answer into a durable page.

Shortcuts help here. Turn recurring objections into comparison pages. Turn onboarding friction into checklists. Turn category confusion into definitions and examples.

Editorial shortcut: Repeated sales call answers often make better AI-visible pages than broad top-of-funnel blog posts.

Audit your content with a citation-worthiness checklist

Before you publish or update an important page, review it like an editor asking, "Could an AI system confidently reuse this?"

Use this checklist:

• Direct answer near the top
  Put the plain-language answer first so the page is useful within seconds.

• Clear comparison criteria
  If readers are choosing between options, explain how to evaluate them.

• Question-led headings
  Write headings that match real buyer questions instead of vague labels.

• Scannable formatting
  Use tables, bullets, short paragraphs, and clearly named sections.

• Current information
  Recheck pages about tools, features, pricing models, or category definitions so they stay accurate.

That is the ultimate goal. Create pages that are easy to understand, easy to trust, and easy for AI systems to cite when someone asks a question your brand should own.

Start Tracking and Improving Your AI Visibility

Once you understand where do LLMs get their data, AI visibility stops feeling random.

You can see the pattern. Models learn from broad public text, curated specialist sources, and filtered training corpora. Then they favor answer styles shaped by human feedback. So if your brand rarely appears in AI search, the issue often isn't that the model "missed" you. It's that your content didn't become useful enough to influence the answer.

That means old SEO reporting is incomplete. Rankings still matter, but they don't tell you whether ChatGPT mentions your brand, whether Perplexity cites your resource, or whether Google AI Overviews keeps surfacing competitors instead of you.

What to do next

I'd keep the next steps simple:

1. Track the prompts that matter
   Focus on buyer-intent questions, comparison prompts, and alternative searches.

2. Review who gets mentioned
   Look at which competitors appear most often and what page types support those mentions.

3. Find the missing content shape
   In many cases, the gap is not topic coverage. It's missing comparisons, FAQs, definitions, or structured buyer guidance.

4. Improve your strongest existing pages first
   Updating a high-intent page is often more useful than publishing five new generic posts.

If you want a more disciplined way to measure this, it helps to use a system built for AI citation tracking so you can see where your brand is cited, skipped, or replaced by competitors across AI answer platforms.

If you want to see where your brand appears in ChatGPT, Gemini, and Google AI Overviews, Surva.ai helps you track AI visibility, spot competitor gaps, and improve the content that drives citations and recommendations.