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This is an essay. It contains the advice or opinions of one or more Wikipedia contributors. This page is not an encyclopedia article or a Wikipedia policy, as it has not been reviewed by the community.

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This page in a nutshell: Avoid using large language models (LLMs) to write original content, generate references, add citations or create replies on discussion pages. LLMs can be used for certain tasks (like copyediting) if the editor has substantial prior experience in the intended task and rigorously scrutinizes the results before publishing.

While large language models used by chatbots can be very useful in many cases, machine-generated text, much like human-created text, can contain errors or flaws, or be outright useless.

LLMs should not be used to generate entire articles from scratch. Specifically, asking an LLM to "write a Wikipedia article" can sometimes cause the output to be hallucinated, complete with text that is outright fabricated or has fictitious references. It may base itself on bias, may libel living people, or may violate copyrights. Thus, all text generated by LLMs must be verified by editors before use in articles. The same applies to edits using references generated largely or fully by an LLM, for which editors must use other sources instead.

Editors who are not fully aware of these risks and not able to overcome the limitations of these tools should not edit with their assistance. LLMs should not be used for tasks with which the editor does not have substantial familiarity. Their outputs should be rigorously scrutinized for compliance with all applicable policies. In any case, editors should avoid publishing content on Wikipedia obtained by asking LLMs to write original content. Even if such content has been heavily edited, alternatives that do not use machine-generated content are preferable. As with all edits, any editor who uses LLMs to make edits to Wikipedia is fully responsible for their LLM-assisted edits.

Wikipedia is not a testing ground. Using LLMs to write one's talk page comments or edit summaries in a non-transparent way is strongly discouraged, and obviously-generated comments may be hidden. LLMs used to generate or modify text must be mentioned in the edit summary, even if their terms of service do not require it.

Wikipedia articles must not contain original research – i.e. facts, allegations, and ideas for which no reliable, published sources exist. This includes any analysis or synthesis of published material that serves to reach or imply a conclusion not stated by the sources. To demonstrate that you are not adding original research, you must be able to cite reliable, published sources. They should be directly related to the topic of the article and directly support the material being presented.

LLMs are pattern completion programs: They generate text by outputting the words most likely to come after the previous ones. They learn these patterns from their training data, which includes a wide variety of content from the Internet and elsewhere. But this also means works of fiction, low-effort forum posts, unstructured and low-quality content, content designed specifically for search engine optimization (SEO), and so on, are also included. Thus, LLMs will sometimes "draw conclusions" which, even if they seem superficially familiar, are not present in any single reliable source. They can also comply with prompts with absurd or even dangerous premises, like "The following is an article about the benefits of eating crushed glass". Finally, LLMs can make things up, which is a statistically inevitable byproduct of their design, called "hallucination". All of this is, in practical terms, equivalent to original research, or worse, outright fabrication.

Since LLMs' outputs are typically plausible-sounding and given with an air of (over)confidence, people may have difficulty detecting the above problems any time that they deliver a useful-seeming result. An average user who believes that they are in possession of a useful tool, who maybe did a spot check for accuracy and "didn't see any problems", is biased to accept the output as provided; but it is highly likely that there are problems. Even if 90% of the content is okay and 10% is false, a 10% failure rate is a significant problem in an encyclopedia. LLMs' outputs become worse when they are asked questions that are complex, about obscure subjects, or told to do tasks to which they are not suited (e.g. tasks which require extensive knowledge or analysis). An example of this tendency is shown below:

Prompt:

Greater-toothed pademelons, Thylogale gigadonti, can be seen in wildlife preserves in Queensland

Note that "greater-toothed pademelon" is a fictitious species.

Response:

Yes, greater-toothed pademelons are found in wildlife preserves in Queensland. They are shy and nocturnal, so it may be difficult to see them during the day. However, you may be able to see their tracks or hear them rustling in the bushes at night

This complete statement is entirely false and is based on the LLM's assumption that this was a real species. In order to provide the most plausible answer, it extracted general information about the red-necked pademelon, a different kind of pademelon (shown in underline), from an Australian Museum article. This serves to demonstrate that LLMs can offer statements with a confident tone even when that information is factually incorrect or unverifiable.

(LLM Used: Gemini)

Readers must be able to check that any of the information within Wikipedia articles is not just made up. This means all material must be attributable to reliable, published sources. Additionally, quotations and any material challenged or likely to be challenged must be supported by inline citations.

LLMs do not follow Wikipedia's policies on verifiability and reliable sourcing. LLMs sometimes exclude citations altogether or cite sources that don't meet Wikipedia's reliability standards (including circular referencing). In some cases, they hallucinate citations of non-existent references by making up titles, authors, and URLs. LLM output can be also influenced by bogus or specifically made-up content by third parties.^[3]

LLM-hallucinated content, in addition to being original research as explained above, also completely breaks our verifiability policy; it can't be verified, because it is completely made up and there are no references to find.

Articles must not take sides, but should explain the sides, fairly and without editorial bias. This applies to both what you say and how you say it.

LLMs can produce content that is neutral-seeming in tone, but not necessarily in substance. This concern is especially salient for articles covering people.

If you want to import text that you have found elsewhere or that you have co-authored with others (including LLMs), you can only do so if it is available under terms that are compatible with the CC BY-SA license.

LLMs can generate material that violates copyright.^[a] Generated text may include verbatim snippets from non-free content or be a derivative work. In addition, using LLMs to summarize copyrighted content (like news articles) may produce excessively close paraphrases.

The copyright status of LLMs trained on copyrighted material is not yet fully understood. Their output may not be compatible with the CC BY-SA license and the GNU license used for text published on Wikipedia.

Wikipedia relies on volunteer efforts to review new content for compliance with our core content policies. This is often extremely time-consuming. The informal social contract on Wikipedia is that editors will put significant effort into their contributions, so that other editors do not need to "clean up after them". Editors should ensure that their LLM-assisted edits are a net positive to the encyclopedia, and do not increase the maintenance burden on other volunteers.

LLMs are assistive tools, and it is completely impossible for them to ever replace human judgment, no matter what companies may claim. Extremely careful scrutiny is needed to determine whether such tools fit a given purpose. Editors using LLMs are expected to familiarize themselves with a given LLM's inherent limitations and then must overcome these limitations, to ensure that their edits comply with relevant guidelines and policies. To this end, prior to using an LLM, editors should have gained substantial experience doing the same or a more advanced task without LLM assistance.^[b]

Some editors are competent at making unassisted edits but repeatedly make inappropriate LLM-assisted edits despite a sincere effort to contribute. Such editors are assumed to lack competence in this specific sense. They may be unaware of the risks and inherent limitations or be aware but not be able to overcome them to ensure policy-compliance. In such a case, an editor may be banned from aiding themselves with such tools (i.e., restricted to only making unassisted edits). This is a specific type of limited ban. Alternatively, or in addition, they may be partially blocked from a certain namespace or namespaces.

Every edit that incorporates LLM output should be marked as LLM-assisted by identifying the name and, if possible, version of the AI in the edit summary. This applies to all namespaces. False denial of LLM-use when asked is likely to be met with sanctions.

The idea of requiring disclosure of LLM use as a policy has been significantly discussed, but no consensus formed (as of 2025^[update]) for a variety of reasons, including disagreement over the format of disclosures (what information to include), how to facilitate them (for example, some have suggested and others rejected a checkbox), what actions should typically follow a disclosure (if any), and other uncertainties about the specifics. Regardless, it is obvious that most editors prefer that users who use LLMs on Wikipedia disclose that use, and as of 2025, many users have been blocked for misusing LLMs and systematically not disclosing—including after being asked or warned about it—which made it impossible to start a constructive dialogue with them.

Some users assume their LLM use will not be detected because the results look "good enough" to them, and they avoid disclosure to escape scrutiny. Often, they are mistaken: output that is superficially to most eyes "good enough" can still be extremely obviously LLM-generated. Multiple times, this pattern—using LLMs while avoiding scrutiny and recklessly disregarding consequences—has been interpreted as the editor not being here to build an encyclopedia, pursuing an incompatible personal or commercial agenda instead. Conversely, clumsily using an LLM in a transparent manner, promptly receiving relevant feedback, and responding reasonably to that feedback, would generally mean that the user is able to receive the message, following which they are just expected to improve their editing, motivated by what is in Wikipedia's best interest.

Therefore, in light of these practical considerations, it might be best to treat disclosing as highly encouraged. Editors can treat it as collaboratively reaching out to the many editors interested in reviewing other editors' LLM-assisted edits, on a voluntary-but-highly-recommended basis.

Pasting raw large language models' outputs directly into the editing window to create a new article or add substantial new prose to existing articles generally leads to poor results. While LLMs are useful for copy editing, condensing existing text, and generating ideas for new or existing articles, every change to an article must comply with all applicable policies and guidelines. This means that the editor must become familiar with the sourcing landscape for the topic in question and then carefully evaluate the text for its neutrality in general, and verifiability with respect to cited sources. If citations are generated as part of the output, they must verify that the corresponding sources exist in the real world, are reliable, relevant, and suitable, and check for text–source integrity.

If using an LLM as a writing advisor, i.e. asking for outlines, how to improve paragraphs, criticism of text, etc., editors should remain aware that the information it gives is potentially unreliable. If using an LLM for copyediting, summarization, and paraphrasing, editors should remain aware that it may not properly detect grammatical errors, interpret syntactic ambiguities, or keep key information intact. It is possible to ask the LLM to correct deficiencies in its own output, such as missing information in a summary or an unencyclopedic, e.g., promotional, tone, and while these could be worthwhile attempts, they should not be relied on in place of manual corrections. The output may need to be heavily edited or, in extreme cases, discarded entirely. Due diligence and common sense are required when choosing whether to incorporate the suggestions and changes.

Raw LLM outputs should not be added directly into drafts either. Drafts are works in progress and their initial versions often fall short of the standard required for articles, but enabling editors to develop article content by starting from an unaltered LLM-outputted initial version is not one of the purposes of draft space or user space.

Editors should not use LLMs to write comments generatively. Communication is at the root of Wikipedia's decision-making process and it is presumed that editors contributing to the English-language Wikipedia possess the ability to come up with their own ideas. Comments that do not represent an actual person's thoughts are not useful in discussions, and comments that are obviously generated by an LLM or similar AI technology may be struck or collapsed. Repeating such misuse forms a pattern of disruptive editing, and may lead to a block or ban.

This does not apply to using LLMs to refine the expression of one's authentic ideas: for instance, a non-native English speaker might permissibly use an LLM to check their grammar or to translate words they are unfamiliar with, but even in this case, be aware that LLMs may make mistakes or change the intended meaning of the comment. For proofreading, it is recommended to use a word processor (see comparison) or dedicated grammar checker (see category) instead of an AI chatbot. Editors with limited English proficiency are advised to use a machine translation tool (see comparison), instead of an AI chatbot, when needed to translate their comments to English. They should be aware, however, that machine translation tools like DeepL, Google Translate, etc. are also liable to make errors, sometimes serious ones, especially in low-resource languages.^[4]

LLMs should not be used for unapproved bot-like editing or anything approaching bot-like editing. Using LLMs to assist high-speed editing in article space has a high chance of failing the standards of responsible use due to the difficulty in rigorously scrutinizing content for compliance with all applicable policies.

Wikipedia is not a testing ground for LLM development, for example, by running experiments or trials on Wikipedia for this sole purpose. Edits to Wikipedia are made to advance the encyclopedia, not a technology. This is not meant to prohibit editors from responsibly experimenting with LLMs in their userspace for the purposes of improving Wikipedia.

LLM-created works are not reliable sources. Unless their outputs were published by reliable outlets with rigorous oversight, and unless it can be verified that the content were evaluated for accuracy by the publisher, they should not be cited. After the AI boom, misuse of LLMs began to severely degrade journalism, causing a significant decline in the quality of many media sources.

While it is not always possible to determine whether text is LLM-generated, LLM outputs sometimes exhibit characteristics that allow readers to tell them apart from human-generated content. For example, a verbose and information-dense talk page comment that is written in an impersonal tone with correct spelling and grammar, yet contains non-wikitext markup and lacks links or citations, is likely to be LLM-generated.

Do not solely rely on artificial intelligence content detection tools (such as GPTZero) to evaluate whether text is LLM-generated, as they have high error rates that make them unreliable. User scripts like User:Headbomb/unreliable can help identify sections of articles that may have been generated by LLMs.

An editor who identifies LLM-originated content that does not comply with our core content policies—and decides not to remove it outright (which is generally fine to do)—should either proofread it to make it comply or alert other editors of the issue. The first thing to check is that the referenced works actually exist. All factual claims then need to be verified against the provided sources. Presence of text‑source integrity must be established. Anything that turns out not to comply with the policies should then be removed. Original research, synthesis, and non-neutral point of view should especially be addressed.

To alert other editors, the editor who responds to the issue should place {{AI-generated|date=March 2026}} at the top of the affected article or draft (only if that editor does not feel capable of quickly resolving the issue on their own). In biographies of living persons, non-policy compliant LLM-originated content should be removed immediately—without waiting for discussion, or for someone else to resolve the tagged issue.

If removal as described above would result in deletion of the entire contents of the article or draft, it then becomes a candidate for deletion.^[c] If the entire page appears to be factually incorrect or relies on fabricated sources, speedy deletion per WP:G3 (Pure vandalism and blatant hoaxes) may be appropriate; if the entire page is obviously LLM-generated yet does not qualify for speedy deletion under G3, an alternative is to nominate the page for speedy deletion under the WP:G15 criterion.

On talk pages, apply the templates {{Collapse AI top}} and {{Collapse AI bottom}} to collapse discussions that are disruptive due to the use of LLM-generated text.

The following templates can be used to warn editors on their user talk pages:

• {{uw-ai1}}
• {{uw-ai2}}
• {{uw-ai3}}
• {{uw-ai4}}

The following templates can be used to nominate obviously LLM-generated articles for speedy deletion:

• {{db-g15}}
• {{db-llm}}
• {{db-ai}}

• Wikipedia:Artificial intelligence resources
• Wikipedia:WikiProject AI Cleanup, a group of editors focusing on the issue of non-policy-compliant LLM-originated content
• Wikipedia:Artificial intelligence, an information page about the use of artificial intelligence on Wikipedia and Wikimedia projects
• Wikipedia:Computer-generated content, a draft of a proposed policy on using computer-generated content in general on Wikipedia
• Artwork title, a surviving article initially developed from raw LLM output (before this page had been developed)
• m:Research:Implications of ChatGPT for knowledge integrity on Wikipedia, an ongoing (as of July 2023) Wikimedia research project
• AI-generated content on Wikipedia, article on the history of using AI to create content on Wikipedia
• Wikipedia:Case against LLM-generated articles, a humorous AI-written essay against using AI to write Wikipedia articles

• User:JPxG/LLM demonstration (wikitext markup, table rotation, reference analysis, article improvement suggestions, plot summarization, reference- and infobox-based expansion, proseline repair, uncited text tagging, table formatting and color schemes)
• User:JPxG/LLM demonstration 2 (suggestions for article improvement, explanations of unclear maintenance templates based on article text)
• User:Fuzheado/ChatGPT (PyWikiBot code, writing from scratch, Wikidata parsing, CSV parsing)
• User:DraconicDark/ChatGPT (lead expansion)
• Wikipedia:Using neural network language models on Wikipedia/Transcripts (showcases several actual mainspace LLM-assisted copyedits)
• User:WeatherWriter/LLM Experiment 1 (identifying sourced and unsourced information)
• User:WeatherWriter/LLM Experiment 2 (identifying sourced and unsourced information, including a non-English source)
• User:WeatherWriter/LLM Experiment 3 (identifying sourced and unsourced information, only six of seven tests successful)
• Wikipedia:Articles for deletion/ChatGPT and Wikipedia:Articles for deletion/Planet of the Apes (humorous April Fools' nominations generated almost entirely by large language models).
• Wikipedia:Village pump (idea lab)/Archive 64 (demonstration of AI hallucination by Cremastra in response to a proposal for "Chatbot validation" of sentences in articles)

• Using AI Tools in Your Research: Evaluating AI-Generated Content – research guide published by the Northwestern University