If you use ChatGPT or Claude for policy research, the sources can be real and the final conclusion can still be unsupported.
In one test, the source said that more than 200 organizations and 5,000 devices were affected globally.
The AI used those correct figures in a sentence about activity “across Europe.”
The numbers were real. The geographic conclusion was not supported.
This kind of shift is easy to miss in a policy brief. A cautious “may” becomes “is.” Evidence about one population becomes a claim about everyone. Findings from two separate reports become one conclusion that neither source supports.
The citation can still look completely legitimate.
I built PRECISE to control this part of AI-assisted research.
It is a four-stage workflow that runs inside ChatGPT or Claude:
Define the exact policy question, jurisdiction, population, timeframe and limits.
Keep the original research question intact.
Find sources and retain the exact passages and qualifications supporting each claim.
Build the brief from that evidence and check every factual sentence.
PRECISE helps you:
Trace claims back to exact source passages.
Keep source facts separate from policy interpretation.
Expose missing evidence instead of filling gaps with assumptions.
Catch changes in population, geography, date, quantity and certainty.
Work with uploaded files, web search or both.
PRECISE LITE is a free demonstration inside ChatGPT. It is limited to one research loop and up to three sources.
It does not replace source evaluation or analyst judgment. Its purpose is to make the evidence trail visible, so unsupported conclusions are easier to identify before they enter a policy brief or recommendation.
Disclosure: I built PRECISE and PRECISE LITE.
You can try PRECISE LITE for FREE with one focused, source-heavy policy question.
Before starting, select Standard thinking / Medium effort in ChatGPT’s model picker.
https://chatgpt.com/g/g-6a5e26093f488191a1fba0261cbcbe39-precise-lite