A generated answer can read smoothly while mixing correct details with unsupported ones. The useful question is whether a reader can trace the important statements back to material that actually supports them.
A practical example
Imagine a team drafting a product comparison. Give the tool a short, dated specification sheet and ask it to identify missing information. Then compare its output against the document. A tool that invents a warranty instead of flagging the gap is creating a review task, even if the rest of the prose sounds convincing.
What to check
- Ask for the location of each important claim in the supplied material.
- Keep the original document and record which version informed the draft.
- Review numbers, quotations and product availability separately from writing quality.
The trade-off
Fluency is helpful for communication, but it is a poor substitute for evidence. Use generation to organize and explain information after deciding which material is reliable enough for the job.
Research context
NIST’s Generative AI Profile considers risks specific to generative systems, including the need to manage unreliable output. It is a risk-management reference, not a certification of a particular tool.
Read the original reference material. The practical examples and evaluation questions above are TheTechGlow’s editorial application of the topic, not reported results from a product test.
Explore Generative Artificial Intelligence for topic coverage, or continue to Artificial Intelligence & Automation for the wider sector.
References and further reading
- nist.govhttps://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence
