Identifying Hallucinations, Inconsistencies and Bias

Audience: professionals who use Claude at work, no coding or API knowledge needed. Verified = checked against Anthropic's documentation or help center on 2026-10-16. Design guidance = widely taught practice, not a documented rule.

Hallucinations

A hallucination is fluent, confident text that is not true or not supported by the material provided. Anthropic describes it as output that can be "factually incorrect or inconsistent with the given context" (verified).

Warning signs (design guidance):

• Very specific details with no source: exact figures, names, dates, page numbers, quotes.
• Citations that look right but do not exist, or exist but do not say that.
• Confident answers about obscure, recent or private topics.
• Answers that fit your hint too neatly (it agrees with what you implied).
• Details that change when you ask again.

Reduce them (verified techniques):

• Allow "I don't know": explicitly give Claude permission to admit uncertainty.
• Ground in quotes: for long documents, ask for word-for-word quotes first, then analyse using only those.
• Verify with citations: ask Claude to cite quotes and sources for each claim; if it cannot find a supporting quote, it should retract the claim.
• Provide the facts yourself and tell Claude to use only the provided material.

Inconsistencies

• Numbers that do not add up; summaries that contradict the body; different answers to the same question; a recommendation that contradicts its own analysis.
• Test it: re-ask differently, ask Claude to list contradictions in its own answer, compare to the source.

Bias

Bias is a systematic tilt: stereotypes, one-sided framing, assumptions about groups, missing perspectives, or favouring whatever is most common in the data. Check by:

• asking "what perspectives or groups are missing?";
• swapping details (a name, gender, region) and comparing the outputs;
• asking for the strongest case on each side;
• reviewing language for stereotypes and loaded terms;
• having a diverse reviewer check sensitive content.

Bias can also come from your prompt (leading questions) and your documents.

Fact-check workflow

1. Highlight every checkable claim.
2. Verify the important ones against originals.
3. Remove or flag what you cannot verify.
4. Record sources.

Exam traps

• Assuming confident means correct.
• Believing a citation because it looks formatted.
• Leading questions that invite agreement.
• Checking only one example for bias.
• Ignoring bias in your own source documents.