AI Deliberator
Guides
How to use AI DeliberatorView all 58 guides

Getting started

Evaluate and export a Council runHow to ask a better decision questionHow to choose models for an AI councilRun your first AI councilUse the Consideration MapContinue a decision with follow-up questionsEstimate and control LLM costs

Everyday decisions

Use an AI council for a career changeEvaluate a leadership role or promotionThink through a relationship decisionDecide whether to move to a new cityCompare degrees, certificates, and education optionsAnalyze a major purchaseUse live evidence mode

Work and teams

Use deliberation for a business strategy decisionSupport a hiring decision responsiblyPrioritize a product roadmapRun a decision pre-mortemMap a stakeholder disagreementAnalyze a policy proposal

Research design

Duplicate and pilot an experimentDesign an LLM deliberation experimentDefine outcomes and dependent variablesConfigure experimental conditions and independent variablesCompare deliberation with homogeneous reconsiderationBuild an independent model ensembleUse randomization, replications, and compute matching

Participant studies

Build a participant interaction studyDesign pre- and post-interaction questionnairesChoose Likert, slider, and questionnaire formatsAdd attention, manipulation, and exposure checksAnalyze and export participant-study resultsBuild a composable experiment timelineMeasure human–AI advice relianceAdd process tracing and cognitive tasksRun a human deliberation conditionBuild a human–AI hybrid deliberation chainRun a text, image, or video stimulus experimentRun a learning and tutoring experimentPlan a powered participant studyMonitor recruitment, cost, and attrition

Behavioral games

Design a behavioral economics gameRun a Dictator GameRun an Ultimatum GameRun a Trust or Investment GameRun a Prisoner’s DilemmaRun a Public Goods GameRun a Stag Hunt GameConfigure game payoffs, incentives, and feedbackManage private game links, consent, and completion

Benchmarks and results

Select benchmark questions and subsetsRun MMLU-Pro in AI DeliberatorRun PolicyBench for public-policy reasoningRun CogToM for social cognitionCreate and read 95% confidence-interval plotsCollaborate and preserve a reproducible experiment recordAnalyze sentiment and quantitative text metricsCompare two Council runs
Workspace

Benchmarks and results

Run MMLU-Pro in AI Deliberator

Use MMLU-Pro categories to test difficult multiple-choice reasoning under controlled council conditions.

The basic idea

MMLU-Pro spans multiple knowledge domains and supports objectively scored comparisons. It is useful for testing whether a protocol generalizes across categories, but aggregate accuracy can hide substantial domain differences.

Example

Run a small chemistry or physics pilot before expanding to a balanced sample across categories and conditions.

How to do it

  1. 1

    Choose MMLU-Pro as the benchmark and inspect available categories.

  2. 2

    Select a category-specific or balanced subset and save a reproducible seed.

  3. 3

    Choose the experimental arm and verify calls, repetitions, and estimated credits.

  4. 4

    Interpret exact-match accuracy with item counts and confidence intervals rather than percentages alone.

What to watch for

  • Public benchmark material may have appeared in model training.
  • Category difficulty and item counts differ.
  • A pilot diagnoses the pipeline; it should not be overinterpreted as a definitive generalization result.

What a useful result looks like

MMLU-Pro supplies a broad, reproducible test of answer accuracy across demanding knowledge categories.