Why make an LLM ask clarifying questions?

The most common LLM failure mode is making wrong assumptions on an underspecified request and confidently running with them. Forcing the model to surface assumptions and ask first catches misunderstandings before they produce wasted or wrong output.

What does the confidence threshold do?

It is the cutoff for proceed-versus-ask. If the model's self-estimated confidence is below the threshold it must ask clarifying questions and stop; at or above it, it states assumptions briefly and proceeds. Raise it when wrong assumptions are costly.

Won't this make the model annoyingly ask all the time?

No — it caps the number of questions and tells the model to skip anything it can reasonably infer from context. Tune the threshold down if you want it to guess more and ask less.

Can I use this with my existing prompt?

Yes. Paste your current prompt in the base field and the tool appends the ambiguity block beneath a divider, so you keep all your existing instructions.

Does the model's confidence estimate mean anything?

It is a rough self-report, not a calibrated probability, but it works well as a behavioral trigger: asking for a number nudges the model to actually evaluate whether it understands the request before answering.

Ambiguity Resolver Prompt

Ambiguity resolver prompt

The single most common LLM failure mode is making a wrong assumption on a vague request and confidently running with it. This tool appends an ambiguity-detection block to your prompt that forces the model to pause, list the assumptions it would have to make, and ask a capped number of clarifying questions before proceeding — so you catch the misunderstanding before it wastes a turn.

How it works

You optionally paste your existing prompt, then set two knobs: the maximum number of clarifying questions and a confidence threshold. The generated block tells the model to (1) list its assumptions in your chosen format, (2) self-estimate its confidence, (3) ask up to N questions and stop if confidence is below the threshold, or (4) state assumptions briefly and proceed if it is at or above. It also tells the model never to ask about things it can infer from context. The prompt is assembled entirely in your browser.

Tips and notes

Raise the threshold for costly tasks. If a wrong assumption means rework or a bad action, set the threshold high (85-95%) so the model errs toward asking.
Lower it for chat. For casual or exploratory use, a lower threshold keeps the model from interrogating the user.
Cap the questions. One to three high-leverage questions beats a long questionnaire — the limit keeps the interaction snappy.
Pair with planning. For multi-step work, combine this with a task-planner block so the model clarifies first, then plans.