Wikipedia has a massive influence over the answers given by AI chatbots like ChatGPT, Google AI Overview, and Meta.ai say. So how can organizations responsibly use Wikipedia to inform AI-generated content?
Does Wikipedia Have a big influence on chatGPT and other aI chatbots?
If you’ve asked an AI chatbot a question lately—something as simple as “What are the safest airlines?” or “How expensive is it to run an EV?”—you’ve probably been influenced by Wikipedia, whether you realized it or not.
Wikipedia is, in fact, the single most significant website for Large Language Models (LLMs) including ChatGPT, Google AI Overview & AI Mode, Meta.ai, Perplexity, and most others. Wikipedia is more than just a source of data for LLMs; it shapes how these systems organize, summarize, and explain information.
In this article, I’ll explain the outsized role of Wikipedia in training LLMs and how Wikipedia can be used strategically to inform the responses offered by chatbots.
How Does Wikipedia Inform the Way LLMs Answer Prompts?
From data source to thought pattern
Most LLMs trained on enormous text datasets that include the tens of millions of Wikipedia articles, which are free to use for any purpose under the Wikimedia Foundation license. That training didn’t just provide facts—it established patterns of reasoning and exposition. When ChatGPT last provided its training data set in 2023, at which time it was still a non-profit organization, Wikipedia accounted for more than 10% of all the training materials.
As models evolved, Wikipedia’s role has expanded. Most AI chatbots now incorporate live web integrations, blending static training data with real-time search results. Our sister firm Citate.ai, which offers highly accurate analytics for LLMs, has found that Wikipedia consistently appears as a link in a wide range of AI responses, including ChatGPT, Google AI Overview, Gemini and Meta.ai.
Still, the influence isn’t uniform. It varies by platform and topic. An AI summarizing “the safest airlines in 2025” might rely heavily on Wikipedia, while a query about “how to start a garden” could draw more from blogs or forums. Even with the exact same question, different users may see dramatically different answers with different sources. That’s why Citate does high-volume sampling every day for every prompt it tracks in order to provide statistically-valid data about LLM answers. It can provide information on exactly how important Wikipedia is for specific queries compared to other sources such as a company website, news sites, review websites or Reddit.
The first step in deciding how Wikipedia might influence a specific query is to check whether any existing page shows up in the answers – but that’s much harder than you might think due to the large variability of AI answers from user to user. More on how to overcome that below.
Of course the absence of Wikipedia as a source might also be a huge opportunity. For example, we know ChatGPT strongly favors Wikipedia for information about companies and people. If there’s no Wikipedia page about the person or company, should one appear it might substantially change LLM answers.
Can You Determine the Influence of Wikipedia on Chatbot Answers?
Citate.ai’s data-driven methodology
Citate.ai offers a structured way to measure the influence of Wikipedia on LLM answers, tracking it over time. Their methodology uses AI and advanced mathematics to collect and analyze a high volume of AI answers, and displays the statistical confidence level that groups of answers are representative. It sounds complicated and it is – which is why Citate has the platform perform the actions and just give you the analysis. From that dataset, they identify which sources most consistently shape LLM answers for a specific query.
Their findings show a nuanced landscape:
- Few sources appear 100% of the time.
- The most frequent ones—often Wikipedia—tend to appear in 70–80% of results.
- In many cases, other platforms such as Reddit, review aggregators, or trade publications play equally significant roles.
This kind of ranked-link analysis helps organizations see not only whether Wikipedia is influential, but how it compares to other domains shaping public-facing AI content.
Can You Use Wikipedia to Inform AI Answers?
Responsible engagement and information strategy
If Wikipedia is a key influence in LLMs in your area of interest, the same complex Wikipedia policies still apply: e.g. verification with reliable source, neutrality point of view, balance and dozens of other policies and best practices. With a proposed new page, you must assess whether a topic meets Wikipedia’s “notability” criteria before submitting a draft for review, And always disclose Conflict of Interest on Wikipedia, and if you have a COI, submit proposed changes or drafts for independent review prior to publication.
If a Chatbot has information blind spots, informing Wikipedia is a great way to catch the attention of ChatGPT and the other chatbots but the information still needs to meet all of Wikipedia’s content guidelines – is this information useful for the encyclopedia? Can it be verified by reliable sources? Is it presented in a neutral and unbiased manner?
Trying to do a major revision/update of an existing page where you have a conflict of interest without Wikipedia experience is a terrible idea, though. Even if you disclose COI and submit the proposal for prior review, there are so many counterintuitive policies and unwritten best practices that you’re unlikely to succeed. The same goes for new pages. Wikipedia is meant to be learned in baby steps over many months. Mistakes are common and to be expected – but you are not going to cut any slack during the COI review process.
The only thing worse you could do would be directly editing, publishing or submitting a page without disclosing conflict of interest – or hiring a black hat to do an undisclosed submission for you. That can lead to a world of trouble. Seek “white hat” Wikipedia consulting from WhiteHatWiki.com if you want professional advice.
For organizations without a Wikipedia presence, it’s worth noting that most companies will not meet the encyclopedia’s rigid notability criteria. Even so, it’s possible to strengthen your broader online profile in ways that indirectly shape how AI interprets your field—Citate.ai literally invented the field now known as Generative Engine Optimization Optimization (GEO) or Answer Engine Optimization and has highly targeted tools and services that can optimize AI answers without a Wikipedia page,
At WhiteHatWiki, we continue to monitor how Wikipedia interacts with evolving AI technologies. Through our partnership with Citate.ai, we aim to understand that relationship empirically—helping clients see where information about their sector originates, and how it circulates through AI-driven systems.
Can Checking a Chatbot Answer Be Misleading?
The probabilistic nature of AI responses
Determining whether Wikipedia shapes what a chatbot says about any subject isn’t as straightforward as checking Google for the order of blue links. LLMs are probabilistic, meaning different users asking identical answers will get different answers, especially if there’s any subjective element to a prompt.
Ask it to solve a calculus problem and you’ll get the same answer. But ask it to research and recommend Smart TVs, listing pros and cons, and the answers given to different users will be completely different. One answer might sing the praises of a RokuTV and Samsung, but omit LGs altogether. Another answer might list some serious cons about buying a certain brand or model – a reputational blow. Still another might favor Google TVs, giving it the most prominence and highest praises.
That makes spot checks unreliable and potentially highly misleading. You might feel great or bad about a response but really have no idea what’s happening across the board. Even most of the LLM analytics platforms that have sprung up are on querying once a day or even once a week – not nearly enough to account for the variance (Citate.ai is the exception.)
It’s especially problematic to rely on spot checks now because some of the LLMs have started to cache the answers for specific user accounts – but just for that one user. So the LLM does not have to spend money to run the query again when you want to go back and check the answers. But as a result, you can’t just run the same prompt a few times yourself to get a sense of the variation between answers – though if you change even one minor word in the query you’ll see you get a different answer.
To understand patterns accurately, you need large-scale samples with a method to determine confidence levels in AI. We have reached the point in search analytics where you really need powerful AI to make sense of another AI.),
(It’s why we partnered with Citate.ai, whose team includes AI professors from Harvard and Stanford. They team invented and patented a foundational model called “Iterative Response Sampling.” They typically collect 50x to 350x the data of competitors.)
Conclusion
A changing relationship between human curation and machine synthesis
Wikipedia’s role in artificial intelligence is dynamic and still unfolding. As LLMs integrate live web data, the boundary between human curation and machine synthesis grows more complex.
Understanding how and where Wikipedia fits into that ecosystem is no longer just an academic question—it’s part of how information about your field, your organization, and even your reputation is formed.
The world of AI is evolving at unprecedented speed. For questions about how LLMs play in affecting your reputation or that of your business, turn to experts at Citate.ai and WhiteHatWiki.
