Preserve the original observation
Keep the source report alongside any summary. An AI tool may remove uncertainty, merge different builds or turn ‘I could not find it’ into ‘the feature is missing’. Those edits change the finding. Ask the author to confirm a materially edited testimonial before publishing it, and do not introduce performance claims or praise that the original user did not provide.
Do not generate fake customer opinions
Creating a named reviewer, avatar and experience from a prompt can mislead readers. The FTC rule addresses fake or false consumer reviews, while stores prohibit review manipulation. Label fictional product examples as examples. A public review should come from the person’s own experience; do not hand testers copy-ready favorable text as a condition of payment or participation.
Use assistance where it is auditable
Useful tasks include grouping reports by issue, translating a reply for human review and turning a real bug report into a reproduction checklist. Remove sensitive information before sending material to a tool and follow your privacy commitments. Assign a person to verify every outgoing support response. Automated confidence should not decide whether a user’s complaint is genuine or deserves acknowledgment.
Working example
A useful instruction is: ‘Summarize these redacted reports by build and task. Preserve uncertainty, quote only statements present in the source and list conflicting observations separately.’ Then inspect each group against the originals. Do not ask the tool to produce ‘ten authentic five-star reviews’. The goal is faster analysis of real evidence, not a larger inventory of invented endorsements.
Your next steps
- Keep original reports and consent records.
- Label fictional examples visibly.
- Review edits and translations before publishing.
- Never invent reviewer identities or experiences.
Official sources
Sources checked October 2, 2026. Review the current provider documentation before acting on requirements.
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