Fact-checking AI answers

Practical guide for reliable verification of language model output

Why is fact-checking necessary for AI?

Language models are designed to generate language as naturally as possible based on patterns, not to speak the absolute truth. This means they can make up facts—a phenomenon known as 'hallucinating'. Without critical checking, you risk adopting incorrect information in your work, reports, or communication.

Step-by-step plan: How to check an AI answer

  1. Explicitly ask for substantiation: Ask the model for the specific sources, documents, or data on which the statement is based.
  2. Perform a cross-check: Verify names, years, statistics, and quotes in independent search engines or primary sources.
  3. Check the generated links: AI models regularly generate URLs that do not exist or lead to dead pages. Always click through to check them.
  4. Test for consistency: Ask the same question in a different way to see if the model remains stable in its answer or if it changes its mind.

Recognizing red flags

Watch out for:

  • Extremely specific figures without clear source attribution.
  • Latin or strange names of studies that cannot be found anywhere online.
  • A lack of context surrounding timelines or locations.

Want to learn more?

Want to dive deeper into how language models work and how to use them optimally? Check out our overview of background articles on our learning module.