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

Work and teams

Prioritize a product roadmap

Compare product initiatives using customer value, evidence, effort, strategic fit, risk, and sequencing dependencies.

The basic idea

Roadmap debates often mix desirability, feasibility, urgency, and executive preference. Separate those dimensions and give models the same evidence so disagreements are about reasoning rather than hidden inputs.

Example

Compare reliability work, a new collaboration feature, a benchmark library, and a visual analytics feature under a fixed engineering budget.

How to do it

  1. 1

    List candidate initiatives and define the decision horizon.

  2. 2

    Provide customer evidence, expected value, effort ranges, dependencies, and risks.

  3. 3

    Ask independent models to rank options before letting them see one another’s answers.

  4. 4

    Use a later turn to challenge the ranking and propose a sequence rather than a single winner.

What to watch for

  • Numerical scores can create false precision when inputs are guesses.
  • Separate mandatory security or compliance work from discretionary features.
  • Revisit the roadmap when evidence changes instead of treating the council result as permanent.

What a useful result looks like

The result should expose why an initiative ranks highly and which dependency or uncertainty could reorder the roadmap.