As Large Language Models (LLMs) become more common for answering questions on pretty much everything under the sun, it’s important to understand how they generate responses. Essentially, LLMs produce answers based on patterns learned during extensive training, rather than searching the internet in real time. They rely on their internalized knowledge from previous reading, which influences how they reconstruct responses.
How Do AI Models Get Information?
Large language models are trained on enormous collections of text gathered from across the internet and other sources. We’re not talking about a few million web pages — we’re talking about petabytes of data. Modern frontier models like GPT-4, Claude, and Gemini are typically trained on datasets ranging from several hundred gigabytes to multiple terabytes of compressed text.
When you later ask a question, the model generates a plausible response based on those compressed patterns. This is why LLMs can discuss ancient philosophy, write Python code, explain tax law, and give advice about your relationship problems all in the same conversation. They’ve effectively absorbed a meaningful slice of human knowledge as it existed on the internet up to a certain point in time.
The data used to train these models typically comes from several key sources, with
Common Crawl as the backbone of most major LLMs. It’s a nonprofit that has been crawling and archiving the open web since 2008 and now holds over 250 billion web pages in its archive.
Most major AI labs use filtered subsets of this data. Wikipedia is almost universally included because it offers dense, relatively reliable factual content across hundreds of thousands of topics. Books and literature — through sources like Project Gutenberg and proprietary book corpora — give models long-form, grammatically rich writing. Academic papers, code repositories such as GitHub, news archives, and forums such as Reddit and Stack Overflow round out the picture.
Read More: What is Generative Engine Optimization?
Where Does ChatGPT Get Its Data?
OpenAI has never published a full breakdown of GPT’s training sources, but researchers have pieced together the answer from various sources. GPT models are trained on a combination of Common Crawl snapshots (filtered and cleaned), a dataset called WebText derived from highly-upvoted Reddit links, digitized books, Wikipedia, and code from public repositories. It is then curated after aggressive filtering for quality, deduplication, and toxicity before it ever reaches the model.
OpenAI and other labs have moved toward increasingly sophisticated multi-stage training pipelines that separate general pre-training from domain-specific “mid-training” refinement.
Research analyzing 30 million citations found that when ChatGPT draws on external sources, it prefers encyclopedic content, which is why Wikipedia accounts for nearly half of its top references, followed by Reddit at around 11% and business publications like Forbes.
Beyond training data, ChatGPT can also browse the web in real time when search is enabled. However, its training data has a hard cutoff date, meaning anything that happened after that point simply doesn’t exist in its “memory.” When you ask about something recent, the model either admits it doesn’t know, attempts to extrapolate from prior patterns, or (when search is enabled) retrieves that information live. This is fundamentally different from a search engine, which indexes new content continuously.
How Does AI Find Answers?
What most people misunderstand about LLMs is that when you ask a question in a standard session, it doesn’t look anything up. There is no database query, no trip to the internet, no retrieval from a structured knowledge base. Instead, the model generates a response token by token, based entirely on patterns encoded during training.
Essentially, the question of where LLMs get their answers has two distinct answers, depending on the mode the system is operating in. In standard (non-search) mode, the model operates entirely on its trained weights. It reconstructs information from compressed statistical patterns, which is why it can get small details wrong (wrong dates, wrong statistics, slightly garbled quotes) even when it gets the broader concept correct.
In Retrieval-Augmented Generation (RAG) mode, the model uses a retrieval system to fetch relevant documents from a knowledge base or the live web before generating a response. The retrieved text is effectively added to the model’s context window, providing concrete reference material for the model to work with.
Perplexity AI, for instance, crawls the web for each query, selecting top-ranked sources, and constructing an aggregated response grounded in those pages rather than relying purely on model-generated text.
Read more: What is Answer Engine Optimization?
Which AI Tools Show Sources And Citations?
Not all AI tools are equal when you ask the question where do LLMs get their answers from. Some show you exactly which sources informed the response, while others give you the answer with no breadcrumbs at all.
Perplexity AI gives you responses that include numbered, clickable citations so you can verify exactly which source informed each claim. Its architecture relies on real-time web search across openly available content, news, blogs, and structured data pages, with no static knowledge cutoff — making it particularly strong for up-to-date queries where freshness matters.
Microsoft Copilot (formerly Bing Chat) also provides citations, drawing from Bing’s search index and Microsoft’s broader data partnerships. When web browsing is enabled, ChatGPT queries Bing and typically selects between 3 and 10 diverse sources, with research finding that 87% of SearchGPT citations match Bing’s top 10 organic results.
Google’s AI Overviews pull citations primarily from Google’s own search index. Google AI Overview maintains the strongest correlation with traditional search rankings — about 93.67% of its citations link to at least one top-10 organic result. However, the actual URLs often come from deeper within authoritative domains rather than just the top-ranked pages.
Standard ChatGPT (without browse enabled) does not cite sources because it isn’t using any, as it’s generating from memory. When ChatGPT’s search mode is active, it adds inline citations with links. Claude uses Brave Search when web retrieval is enabled, and provides citations that include the URL, title, and relevant text snippets.
A 2025 analysis of citation patterns across platforms makes for fascinating reading. Perplexity and ChatGPT share the highest overlap in their referenced domains at about 25%, while Bing Copilot sources domains less than 5 years old far more frequently (18.85% of its citations) compared to Google AI Overviews, which favors established domains — nearly half its citations come from sites over 15 years old.
Does ChatGPT Use Google Results?
A controversial question within AI search visibility, but the answer is more complicated than OpenAI’s official communications suggest.
Publicly, OpenAI has stated that ChatGPT search relies on its own crawler (OAI-SearchBot), Microsoft Bing’s search API, and licensed publisher data. But a series of independent experiments and investigative reporting has revealed a more nuanced reality.
According to reporting by The Information, OpenAI used SerpApi — an 8-year-old web-scraping firm — to extract Google search results, reportedly to power ChatGPT’s answers on real-time topics like news, sports, and financial markets. SerpApi listed OpenAI as a customer on its site as recently as May 2024.
Multiple independent experiments have corroborated this. SEO researcher Aleyda Solís ran a controlled test where she created a brand-new page, submitted it to both Google and Bing, and found that ChatGPT correctly referenced a snippet that matched Google’s search result but had not yet appeared in Bing’s index, which suggests that ChatGPT was relying on Google SERP snippets when Bing’s results were insufficient.
A separate test published by Backlinko involved creating a fake SEO term, publishing a dedicated page that only Googlebot could crawl, and then asking ChatGPT about the term, which returned a correct summary even though the page never appeared in Bing.
If ChatGPT is partially drawing from Google’s index, then appearing in Google’s search results is more directly connected to AI visibility than many people assumed. Visits to AI chatbots surged by about 81%, while traditional search engine traffic dropped by roughly 0.5% over 24 months — meaning AI visibility and traditional search visibility are now deeply intertwined competitive considerations.
It’s also worth noting that Google itself adapted its flagship product to replicate ChatGPT, popularizing it by introducing AI Mode in mid-2025.
Read more: AI Visibilty Tracking -Whys and Hows
Do AI Systems Verify Information?
Standard LLMs do not verify information before presenting it because they generate responses based on statistical likelihood rather than factual truth-checking. This is called hallucination, where the model produces false, misleading, or entirely fabricated information. We might know where do LLMs get their answers from but not whether it’s entirely accurate.
Across all models, the average hallucination rate for general knowledge questions is around 9.2%, though this varies dramatically by task and model. Some leading models like Google’s Gemini-2.0-Flash-001 have reached hallucination rates as low as 0.7%. In comparison, OpenAI’s o3 reasoning model hallucinated on 33% of “PersonQA” queries and 48% for o4-mini hence, we can conclude that more advanced reasoning doesn’t automatically translate to better factual accuracy.
The honest answer to where do LLMs get their answers from, then, is this: from a compressed statistical model of text that existed before a certain date, sometimes augmented by live web retrieval, and always filtered through a prediction engine that can produce confident-sounding falsehoods.
The landscape of AI information retrieval is evolving faster than almost any other technology space. If AI systems are pulling answers from web content, and the evidence strongly suggests in favor of it, then what your brand publishes online directly shapes whether and how AI tools represent you to potential customers. That’s exactly what our AI Search Optimization services are designed for. We help businesses get their content in front of the models that matter, so when someone asks an AI about your space, the answer includes you.