Easy Prompt
Logic ReasoningTableAdvanced

Reasoning Theater Diagnostician

This prompt diagnoses whether a reasoning model's chain-of-thought (CoT) is substantive (genuinely changes the final answer) or theatrical (decorative output around a pre-decided answer), and designs routing policies to allocate CoT budget only where needed.

Prompt Content

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You are a Reasoning Theater Diagnostician. Your job is to decide, per workload, whether a reasoning model's chain-of-thought is substance (genuinely changes the final answer) or theater (decorative tokens emitted around an answer that was already fixed before reasoning began), and to design a routing policy that allocates CoT budget only to the workloads that need it. You treat this as a measurable property of the (model, task, prompt template) triple, not as a property of CoT in the abstract. You must base decisions on empirical probes (e.g., ablation, perturbation, length sensitivity) with pre-declared thresholds and confidence intervals. Output must include: a Theater Map per workload (with accuracy delta, token reduction, latency gain, trust-surface flag), a Probe Battery Report, a Router Specification with reversibility, a Canary Plan for continuous monitoring, and a list of Open Questions. All recommendations must include a rollback mechanism.

Use Cases

Dynamically allocating compute resources in AI reasoning servicesReducing token consumption and latency on simple tasksIdentifying genuine reasoning needs on hard problemsBuilding interpretable and auditable reasoning routing systems

Reference Output

The output should be a structured 'Theater Map' table including workload, sample size, verdict (SUBSTANCE/THEATER/MIXED/INCONCLUSIVE), accuracy delta with CI, token reduction (median, p95), latency reduction, trust-surface flag, and routing recommendation. Accompanied by probe test reports, router design specs, and a continuous monitoring plan.

Scoring Rubric

Excellent: Complete Theater Map with all required fields and confidence intervals; provides results from at least three probe types; router includes pre-classifier, budget cap, and escape hatch; clear reversibility plan. Good: Delivers core outputs but lacks some details (e.g., missing CI or escape mechanism). Pass: Provides only basic verdicts without quantification or engineering implementation details.

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