Here's the prompt structure I use so an AI report generator stops mixing facts with opinions

The recurring problem with generated reports is that findings, guesses, and recommendations all blur into the same confident paragraph. I now force the model to keep them in separate buckets, and it's much easier to trust. The prompt: ``` Write a report from the material below. Material: """ [paste data / notes / transcript] """ Use exactly these sections: What the data shows (only facts present in the material, each with the line it came from) What is likely but not certain (label each as an inference, not a fact) What is missing (gaps you'd need filled to be confident) Recommendations (each tied to a specific finding from section 1) Rule: nothing goes in section 1 unless it's directly in the material. If you're unsure, it belongs in 2 or 3. ``` Why it works: separating "facts" from "likely" from "recommendations" stops the model from laundering a guess into a finding, which is the thing that gets you in trouble when someone acts on the report. Tying every recommendation back to a section-1 fact kills the generic advice that could apply to any company. Section 3, "what is missing," is the sleeper. It usually tells me what to go collect before the report is worth sending. What structures do you use to keep generated reports honest? submitted by /u/Comfortable_Guide859 [link] [comments]