About This Article
This article was created using an automated generation workflow leveraging generative AI. It organizes how verifying LLM responses changes when explicitly requesting "evidence" and "unknowns" by altering just a single instruction, formatted so you can test it using safe dummy data.
Verification Status: 🧪 Prompt design example created, reproducibility with specific models not yet confirmed.
When using LLM responses for work, rather than having them generate "seemingly correct text",it is more important to have them output in a format that humans can verify.
First, Try with the Same Input
We prepare a dummy internal memo.
新システムは10月導入予定。担当は総務部。 費用は未確定。研修日は9月25日を候補として調整中。
First, we make this request.
このメモを要約してください。
Next, without changing the input, we add just one instruction.
このメモを要約してください。 各項目に「入力中の根拠」を付け、入力から判断できないことは「不明」と書いてください。
Look Here
What we are comparing is not the quality of the writing.
Whether costs were arbitrarily supplemented
Whether September 25 was treated as a confirmed date
Whether each conclusion can be traced back to the source text
Why It Works
Asking an LLM to provide an evidence column does not guarantee factual accuracy. However, it makes it easier for humans to cross-check "which input led to that conclusion."不明Allowing [blank] also helps in identifying forced gap-filling.
Change One Part
不明Change [X] to [Y] and use it as a checklist that the person in charge can fill in later.要確認Transforming the output from a finished sentence into a confirmation sheet makes it harder to adopt AI responses blindly.
For Professional Use
For meeting minutes, regulation summaries, incident reports, and product comparisons, structuring outputs into three columns—"Response, Evidence, and Unknowns"—makes them easier to review. Please check your organization's usage policies first to determine whether confidential or personal data may be entered.
Conclusion
A good prompt is not a long spell. Specifying an output structure that humans can verify is what works in practice. Change just one instruction with the same input and observe how the verification time changes.
