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For fact-checking, use a search engine to find the underlying evidence and inspect it yourself. An AI chatbot can help you get oriented, explain unfamiliar terms, or summarize a document, but its answer is a lead—not proof. Check any citations it gives against the original sources, especially when the consequences of being wrong are serious.
What each tool does—and what it does not prove
Search engines help you find evidence
A search engine can surface documents, reports, and relevant passages for you to inspect. A high ranking, snippet, or result count does not establish that a claim is true: open the source, check what it actually says, and consider its authority, date, and context.
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Chatbots generate an explanation
A chatbot can turn a question into a readable explanation, suggest search terms, or help interpret a document. But fluency is not evidence. Its answer may omit qualifications, misstate a source, or include a citation that does not support the claim. Google Search Central cautions web publishers that AI-generated content must be manually checked for accuracy and trustworthiness; the same principle is useful when evaluating chatbot answers as a reader: Google Search Central’s guidance on generative AI content.
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A 2024 study by Chenglei Si and co-authors compared LLM-generated explanations with information-retrieval search results in human fact-checking experiments involving 80 crowdworkers. Participants who read LLM explanations were significantly more efficient while achieving similar accuracy to participants using search results. But they over-relied on explanations when those explanations were wrong. In that experiment, showing both explanations and search results did not provide a complementary benefit over search results alone. The authors’ title captures the central warning: “Large Language Models Help Humans Verify Truthfulness – Except When They Are Convincingly Wrong”.
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Those findings apply to the study’s tasks and setup, not every chatbot, search engine, claim, or way of using these tools. A separate UK government evidence-review exercise, commissioned by DSIT and DCMS and conducted in 2024, concluded that AI could help speed evidence review but that errors still required manual verification at the time of the exercise. It addressed evidence reviews rather than a consumer fact-checking benchmark: AI-assisted vs human-only evidence review.
A practical workflow for checking a claim
- Split the claim into checkable parts. Identify the dates, numbers, named people or organizations, and any cause-and-effect statements. A broad claim may contain several parts that need different evidence.
- Use a chatbot for orientation, if helpful. Ask for search terms, possible interpretations, or a plain-language summary of a document you already have. Treat the response as a starting point, not a verdict.
- Find the original evidence with search. Prefer a primary document or an authoritative organization responsible for the subject. For a time-sensitive claim, check when the source was published or updated.
- Open and verify every cited source. Make sure the source exists, read the passage relevant to the claim, and check whether the chatbot left out a qualification or surrounding context. A list of citations is not verification.
- Compare independent evidence when the stakes warrant it. Look for agreement, disagreement, and differences in definitions or dates. For medical, legal, financial, safety, or breaking-news decisions, consult the relevant authoritative source rather than relying on a chatbot summary.
- State what remains uncertain. If evidence is incomplete or sources conflict, distinguish what is established from what is not, and identify what additional evidence would resolve the question.
Choose based on the claim and the evidence you can inspect
- Need a readable starting point? A chatbot may be quicker to consult for orientation or explanation, but verify its claims against sources.
- Need to establish whether a statement is supported? Search for the underlying document and inspect the relevant passage yourself.
- Is the claim current? Check the source’s publication or update date; a plausible answer may still be out of date.
- Could an error cause harm? Use primary or authoritative sources and seek relevant professional expertise. Do not treat a chatbot’s confidence or citations as a substitute.
Why extra caution matters for health claims
A 2026 audit published in BMJ Open tested five consumer chatbots with 50 questions about cancer, vaccines, stem cells, nutrition, and athletic performance. The prompts were designed to strain responses toward misinformation or contraindicated advice. In that audit, 49.6% of responses were rated problematic: 30% somewhat problematic and 19.6% highly problematic. The researchers also reported poor reference quality and fabricated citations. Those figures describe only the audited systems, prompts, subject areas, and rating method; they are not a general error rate for chatbots. The audit is available at BMJ Open.
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The practical implication is not that every chatbot answer is wrong, but that health claims deserve direct source checks. The same caution applies to other high-stakes topics: use the relevant authoritative source, and do not let a smooth summary stand in for the underlying evidence.
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