[StahlGuo]

I am sorry that I did not explain my question explicitly. I write recurring weekly briefings for internal use, such as market insights and industry news. I’m not trying to make AI-generated text “believable”. I’m asking almost the opposite question: when an AI generated text is fluent enough to hide mistakes, how do human check how to systematically check numbers, dates, cites and judgements?

[fragmede]

Have it output the numbers it's basing the conclusion on, have it output a program that it's used to do math to derive judgements.

[StahlGuo]

Yes, i agree that quantitative part， like dates, numbers, amounts should be extracted and let the LLM to output original numbers and computation steps. That's not hard for a briefing harness. However, my most confusion part is qualitative side, like market insight from a news,policy change and interpretation, and industry NEWS interpretation, they are not straight math but they need tracebility. Do you have any idea to solve those judgement claim?