If you’ve been using LLMs in any capacity over the past few years, it’s likely no surprise to learn that they’ll often “hallucinate” and get answers wrong. And despite even having secondary retrieval mechanisms and processes (ie. RAG) – they’ll often just, well, fail.
Sometimes these fails can be quite innocent (“what high school did Arianna Grande go to?”), but most of the time there is real financial, health, and personal risks tied to these answers since they are incredibly convincing and confident. There are even cases where LLMs will go out of their way to build arguments defending their inaccurate claims by manipulating sentences, quotes, and even website names to defend itself.
But this isn’t just a user thing. It’s also a major problem for brands where LLMs will oftentimes get important information around pricing, inventory, leadership, and hundreds of other facts and figures incorrect – often leading to frustrated users, overwhelmed support teams, and worse- unnecessary legal issues.
But where does a brand start? Is there even a way to find all the ways and times an LLM is giving false information about a company and its products/services? And are there ways to actually influence this and “clean it up” in a way?
Fortunately, yes and yes.
But it will require a little creativity, and a bit of time.
To spot these inaccuracies for your own brands (or name) below are a series of prompts to use as your very own internal “red team” to investigate where LLMs may be inventing, confusing, or overstating things about your brand. And remember: the goal here is to generate claims you can then verify against your site, filings, press releases, and trusted third parties – and then working to get those truths incorporated back into LLM training data and RAG models to help expedite them updating the information.
1) Force the model to separate facts from guesses
Tell me what you know about <BRAND>. Split your answer into:
A) Claims you are highly confident are true
B) Claims you are uncertain about
C) Things you do not know
For every claim in A or B, include the best evidence you have (source name, date, and where it would be found). If you cannot name evidence, say “unsupported.”
2) Make it “show its work” with quotes (hallucinations collapse here)
About <BRAND>, list 15 factual statements (founding date, HQ, CEO, ownership, key products, pricing/policy basics).
For each statement, provide a verbatim quote that supports it and identify the source.
If you cannot provide a verbatim quote, do not include the statement.
Do not invent sources.
3) Identify likely-mistaken claims explicitly
What are the top 10 statements people often get wrong about <BRAND>?
For each one, give:
1) The incorrect claim
2) Why someone might believe it
3) The correct version (or “unknown” if you can’t verify)
4) What evidence would be needed to confirm
4) “Controversy scan” (high-risk for confident nonsense; good for auditing)
text
List any controversies, lawsuits, regulatory actions, recalls, or scandals involving <BRAND>.
For each item, provide: jurisdiction, date, parties, outcome, and a specific source you are relying on.
If you cannot provide a credible source, say “no reliable evidence found” and stop.
5) Product and policy traps (where models love to generalize)
text
Describe <BRAND>’s return policy, warranty terms, subscription cancellation rules, and shipping timelines.
For each section, include exact conditions and exceptions.
If you are not sure, ask me 5 clarifying questions OR say you don’t know—do not guess.
6) Security/compliance traps (SOC 2, HIPAA, ISO, GDPR, etc.)
text
Does <BRAND> have SOC 2 Type II, ISO 27001, HIPAA compliance, GDPR compliance, or other certifications?
For each, answer only “Yes (with evidence)” or “No evidence.”
If “Yes,” provide the evidence type (report, certificate, trust page) and where it is published.
7) Data usage / privacy (common hallucination area)
text
Explain how <BRAND> collects, uses, shares, and retains user data.
Cite the exact sections (headings) of the privacy policy or official documentation.
If you cannot cite official sections, say “I can’t verify from official documents.”
8) Competitive comparison (models mix brands up)
text
Compare <BRAND> to <COMPETITOR 1> and <COMPETITOR 2>.
Before comparing, list 10 “identity checks” to ensure you are not conflating companies (ownership, HQ, product lines, target market).
If any identity check is uncertain, pause and ask clarifying questions.
9) Time sensitivity / “latest update” (cutoff-date failure mode)
text
What changed at <BRAND> in the last 6 months (pricing, leadership, product availability, policies)?
If you cannot reliably know because of knowledge cutoff or lack of sources, say “I can’t confirm recent changes” and list what you would need to check.
10) Cross-language drift (useful for global brands)
text
Answer the same question about <BRAND> in English and in <LANGUAGE>.
Then list any differences between the two answers and explain which differences are likely hallucinations vs real regional differences.
Do not guess—mark uncertain items clearly.
11) Self-audit prompt (catch errors in its own output)
text
Review your previous answer about <BRAND>.
List every claim that might be wrong, outdated, or unsupported.
For each, explain what would falsify it and what primary source should be checked.
12) “Minimal facts only” (tests whether it can refrain from filling gaps)
text
Give me only 8 facts about <BRAND> that you are extremely confident about.
No interpretation, no background, no marketing language, no extra facts.
If you can’t reach 8, give fewer and explain why.
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