What Websites Are Most Often Visited By LLMs?

Do LLMs Read the Whole Web — or Just a Few Sites That Shape Answers?

"LLMs read the whole internet." But the real question is: which sites do LLMs actually see most — and how does that shape your brand's discoverability in LLM training data and AEO for LLMs? This piece gives concise, practical answers about Common Crawl inputs, the domains that dominate LLMs, and direct steps to boost your visibility in generative AI results.

Which sites matter most? LLMs are trained heavily on large public crawls (like Common Crawl), major reference sites (Wikipedia), prominent news publishers, large forums and Q&A hubs, and popular code repositories. That concentration creates predictable biases: if those sources cite you, your content is far more likely to surface. For non-technical teams, the tactic is simple — get authoritative citations, publish clear, reusable summaries, and test common prompts (PromptScout-style checks) to measure real-world visibility. Focus on quality, structured metadata, and syndication to trusted outlets to improve AEO outcomes and your presence in generative AI answers.

Meta title: Which Sites Do LLMs Read Most? — AEO Guide Meta description: Discover which domains dominate LLM training data, how that biases answers, and concrete AEO steps to get your site cited by LLMs. CTA: Learn how. lang="en-US" (swap local forum examples like Reddit for regional platforms where relevant).

Do LLMs browse your site live? No — they learn from snapshots and curated corpora

LLMs don’t surf the web in real time; they train on large, static snapshots of web content called corpora. A training snapshot is a frozen collection of crawled HTML and text, which is cleaned, deduplicated and tokenized into model-ready pieces before use. Tokenization breaks visible text (and often cleaned HTML) into subword units the model learns from, and filtering removes spam, duplicates, non-target languages and private data so only high-quality signals remain.

Common public sources include Common Crawl (CC-MAIN / CC-INDEX), C4 (Colossal Clean Crawled Corpus), WebText/WebText2-style scraped datasets, The Pile, and various filtered news or journal crawls. Typical provenance varies: Common Crawl runs monthly to quarterly crawls, C4 is a cleaned subset of web crawls, and WebText-style collections focus on socially linked pages. Many datasets are available as BigQuery public tables or GitHub mirrors and use heuristics like spam filters, deduplication, and language detection. To check presence: (1) search Common Crawl indices for your hostname, (2) query public BigQuery datasets for domain strings, (3) look for backlinks on Reddit/GitHub/Wikipedia that drive WebText-style captures, and (4) inspect server logs for crawler activity. For SEO and geo queries, try long-tail searches like “is my site in Common Crawl UK” or “does ChatGPT know my local news site” and consider adding Dataset and DataDownload schema with sameAs links to authoritative sources.

Which sites actually feed LLMs — the heavy hitters and what to show

If you want to know which domains dominate model training, think of a short list: wikipedia.org, github.com, arxiv.org plus major news outlets, Stack Overflow and patent archives. Estimates from Common Crawl and C4 analyses suggest a handful of domains contribute the largest token shares (ranges like 1–5% for top encyclopedias or code hosts, and many others at <1%). For clarity present a sortable table with columns: domain, approximate % of tokens (estimate), primary content type, and source. Note numbers are ranges or estimates when exact counts aren’t public.

Why these sites matter and where they come from: long-form factual pages and encyclopedias provide stable facts, code hosts teach patterns and syntax, news sites supply timely prose, and preprint servers add technical depth. The web crawl is strongly English-dominant, leaving many languages underrepresented. Helpful visuals include a top-25 domain table (with alt text), a bar chart of content-type token share, and a world heatmap of origin. For SEO/GEO, localize example outlets for regional readers, add hreflang for translations, and mark rankings with schema ItemList and representative pages with CreativeWork.

Why LLMs Miss Local and Niche Sites — And How to Fix It

LLMs tend to favor content amplified by big link hubs and broadly crawled sources, which creates four key biases: link-driven selection (Reddit, GitHub, Wikipedia amplification), aggressive filtering that weeds out niche pages, language/region skew toward English and major markets, and topical skew toward tech, news, and code. Because of these skews, common failure modes include poor local news coverage, weak handling of domain-specific jargon, and reliance on outdated or paywalled facts—leaving smaller publishers invisible or misrepresented.

To measure and shift this, follow a concise checklist and experiment plan: seed a representative prompt set, run N trials (suggest 100) across multiple model families, record how often your domain is cited and the model's confidence, and track changes after site updates. Capture metrics like citation rate, rank position, snippet similarity, and hallucination rate. For practical testing, start by seeding a set of prompts at baseline; use short intent prompts and longer context prompts; run experiments, extract URLs, then implement two AEO changes (add FAQ & schema, update canonical page) and re-run after 2–4 weeks. Tactical AEO steps: publish high-signal long-form pages (1,200+ words) with clear headings and FAQs, add well-formed schema types (Article, FAQPage, HowTo, Dataset), ensure crawlability and persisted HTML, earn contextual links from high-domain sites, and localize with hreflang and regional backlinks. KPIs to watch: citation rate improvement, time-to-first-citation, snippet accuracy, and reduced hallucination. Run your first PromptScout experiment now and download the checklist/CSV template to get started.

References

• Language Models are Few-Shot Learners
• Better language models and their implications — OpenAI blog
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5) / C4
• Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
• The Pile: An 800GB Dataset of Diverse Text for Language Modeling
• Datasheet for the Pile
• OpenWebText — replication of OpenAI’s WebText, Zenodo
• Overview of OpenAI Crawlers — GPTBot / OAI-SearchBot docs
• No Robots(.txt): How to Ask ChatGPT and Google Bard Not to Use Your Website for Training — Electronic Frontier Foundation
• Capturing Growth with AIO / AEO / GEO — it's part of SEO — SEO Savages