Does this actually reduce hallucinations?

It reduces confident bluffing on questions the model cannot answer, which is a major source of harm. It does not make the model omniscient — a model can still be wrong while sounding calibrated. Pair it with verification for anything critical.

What does the risk level change?

Risk level sets the abstention threshold. At high risk the model is told to refuse and say it is unsure rather than guess; at low risk it can offer a best-effort answer while still flagging uncertainty. Match it to the cost of being wrong.

Which uncertainty format should I pick?

A confidence label (high/medium/low) is easy for people to read. A percentage is better when you parse output programmatically and want a threshold. Hedging language reads most naturally in prose but is harder to act on automatically.

Will it make every answer wishy-washy?

No. The instructions ask the model to be confident when it has good grounds and to hedge only when it genuinely lacks them. Well-calibrated output is decisive where it should be and cautious where it must be.

Prompt Confidence Calibrator

Prompt confidence calibrator

The most dangerous failure mode of an LLM is not being wrong — it is being wrong confidently. By default models rarely say “I don’t know”; they produce a fluent, authoritative answer whether or not they have the facts. For anything where a mistake costs money, time, or trust, you want the opposite: a model that signals its confidence and abstains when it should. This tool appends calibrated uncertainty instructions to your prompt so the model stops bluffing.

How it works

You paste your factual prompt and pick a risk level and an uncertainty format. The tool appends an instruction layer that does three things: it sets an abstention threshold (at high risk the model must say it is unsure rather than guess), it tells the model to attach a confidence signal in your chosen format — a high/medium/low label, a percentage, or hedging words — and it reminds the model to be decisive when it does have good grounds, so the output is not uniformly wishy-washy. Your original instruction is preserved verbatim; only the calibration layer is added.

Tips and notes

Match risk to consequence. Use high risk for medical, legal, financial, or safety questions; low risk is fine for brainstorming where a wrong guess costs nothing.
Use percentages for automation. If a downstream system decides whether to act on the answer, a numeric confidence lets you set a clean threshold.
Calibration is not verification. A model can be confidently wrong even with these instructions. For critical answers, still check the facts independently.
Combine with a reasoning trace. Seeing both the confidence level and the reasoning makes it far easier to judge whether the stated confidence is justified.