capability elicitation

**Capability Elicitation** is **the process of designing prompts and evaluation setups that reveal the strongest reliable model performance** - It is a core method in modern AI evaluation and safety execution workflows. **What Is Capability Elicitation?** - **Definition**: the process of designing prompts and evaluation setups that reveal the strongest reliable model performance. - **Core Mechanism**: Different scaffolds can unlock latent capabilities that simple prompts fail to expose. - **Operational Scope**: It is applied in AI safety, evaluation, and deployment-governance workflows to improve reliability, comparability, and decision confidence across model releases. - **Failure Modes**: Weak elicitation can underestimate model ability and distort system planning decisions. **Why Capability Elicitation Matters** - **Outcome Quality**: Better methods improve decision reliability, efficiency, and measurable impact. - **Risk Management**: Structured controls reduce instability, bias loops, and hidden failure modes. - **Operational Efficiency**: Well-calibrated methods lower rework and accelerate learning cycles. - **Strategic Alignment**: Clear metrics connect technical actions to business and sustainability goals. - **Scalable Deployment**: Robust approaches transfer effectively across domains and operating conditions. **How It Is Used in Practice** - **Method Selection**: Choose approaches by risk profile, implementation complexity, and measurable impact. - **Calibration**: Test multiple prompt protocols and report both baseline and best-elicited performance. - **Validation**: Track objective metrics, compliance rates, and operational outcomes through recurring controlled reviews. Capability Elicitation is **a high-impact method for resilient AI execution** - It produces more accurate assessments of what a model can actually do.