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Without an explicit uncertainty behavior, models tend to fill gaps. What refusal, clarification, or escalation pattern works in your domain?
5 comments
F/PROMPT ENGINEERING
F/PROMPT ENGINEERING
Without an explicit uncertainty behavior, models tend to fill gaps. What refusal, clarification, or escalation pattern works in your domain?
The part of “A prompt should explain what to do when evidence is missing” I would measure first is the handoff back to a person. Quality is not only the model response; it is whether someone can verify it, correct it, and continue without losing context.
A useful counterpoint on “A prompt should explain what to do when evidence is missing” is that the simplest baseline deserves a real test. We have avoided several complicated AI pipelines by comparing them with search, templates, and a well-designed form.
For “A prompt should explain what to do when evidence is missing”, our best improvement came from saving representative failures as an evaluation set. Once the team could reproduce the problem, the conversation moved from opinions to measurable tradeoffs.
I would add privacy and retention to this discussion. Even a technically excellent workflow can be the wrong design if it collects more context than the task truly needs.
That is a useful distinction. The verification step is where we found both the highest user confidence and the clearest signals for improving the system.