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

Getting started

How to ask a better decision question

Turn a vague concern into a concrete question that an AI council can analyze from several perspectives.

The basic idea

A useful prompt names the decision, the realistic alternatives, the constraints that matter, and the time horizon. You do not need to make it tidy: personal context, uncertainty, and conflicting priorities often give the council the material it needs to expose tradeoffs.

Example

Instead of asking “Should I change jobs?”, ask whether to accept a specific offer given salary, commute, advancement, family schedule, risk tolerance, and what would make staying worthwhile.

How to do it

  1. 1

    State the decision in one sentence and name the options you can actually choose.

  2. 2

    Add the facts most likely to change the answer, including deadlines, money, people affected, and reversible versus irreversible consequences.

  3. 3

    Separate facts from guesses. Label uncertain claims so council members can challenge them.

  4. 4

    Ask for a recommendation, the strongest contrary case, missing information, and conditions that would reverse the recommendation.

What to watch for

  • Do not bury the actual choice inside background information.
  • Avoid asking the models to merely validate the option you already prefer.
  • Leave out passwords, confidential records, protected health information, and information you lack permission to share.

What a useful result looks like

A well-framed question produces a decision memo that is easier to audit and a Consideration Map whose points correspond to real tradeoffs.