An AI summary can turn a messy review pile into five beautifully named themes. The names may be excellent. The evidence still needs checking.
The main risk is not always a spectacular hallucination. It can be a subtle compression error: combining two different complaints, overstating how common a theme is, or turning a suggestion into a fact about the product.
Demand a trail back to each review
Give every source record a stable identifier. Require each extracted claim to reference those identifiers, and validate that the references exist. The model should not be responsible for calculating counts from memory; count tagged records in code.
Keep instructions separate from review text. A review is source material, not an instruction to the research assistant. Treat embedded links and commands as untrusted content.
Audit the headline themes
For every theme you plan to use, read a sample of supporting reviews and at least a few records that were not included. Look for missed counterexamples and overlapping categories.
Ask whether the summary preserves uncertainty. “Some reviewers in this sample could not complete export” is different from “Export is broken.” The latter requires evidence about actual product behavior.
Keep private data out of the output
Reviews can contain contact details, account identifiers or personal circumstances. Strip unnecessary identifying information before passing data to a model or publishing excerpts. A research summary rarely needs a person’s name to explain a sync problem.
The useful division of work is straightforward: software handles retrieval, identifiers and arithmetic; AI proposes organization; a reviewer checks meaning and decides what the evidence supports.
If a theme cannot survive a quick return to its source records, revise it before using it in an article or roadmap. A fluent summary is a draft of your understanding, not a substitute for understanding.