next-best-questions

To identify valuable clarifying questions, use a decision theory framework that estimates the value of information for each candidate question based on its potential to change the outcome and the stakes involved. This optimizes information gain before proceeding.

The value of information measures how much a potential clarifying question can change a decision's outcome and its associated stakes. It is needed when making decisions involving ambiguous or underspecified problems to ensure you gather the most impactful information first.

Generate candidate questions for ambiguous problems by running a local Ollama model against your problem statement. The model applies decision theory principles to automatically produce a list of clarifying questions prioritized by their estimated value of information.

Yes, local Ollama models are required to estimate the value of information and generate candidate questions. The Skill uses these local models to process your problem statement and apply decision theory principles for decision support.

The best way to prioritize clarifying questions is by measuring their value of information based on outcome impact and stakes. This approach distinguishes critical questions from low-value ones, ensuring you focus on information that actually changes your decision.

You can use this question generation framework for any decision-making process involving ambiguous or underspecified problems, regardless of scale. It evaluates the stakes involved and potential outcome changes to identify valuable questions for both small and large projects.