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

Everyday decisions

Use an AI council for a career change

Analyze compensation, meaning, advancement, commute, risk, family effects, and reversibility before changing jobs.

The basic idea

Career decisions combine measurable terms with values that are easy to underweight. A council is most useful when you provide the concrete offer, your current baseline, and the personal costs that never appear in a job description.

Example

Compare a 20 percent raise and leadership opportunity against Atlanta traffic, a longer day, organizational politics, a six-year commitment, and reduced flexibility.

How to do it

  1. 1

    List current-role and new-role facts in parallel, including total compensation and time costs.

  2. 2

    Describe what success would look like after one year and after five years.

  3. 3

    Assign one model to advocate for the move and another to make the strongest case for staying.

  4. 4

    Ask the chair for a recommendation, negotiation targets, and a reversible next step.

What to watch for

  • Do not let salary stand in for total quality of life.
  • Check claims about contracts, benefits, and employment law with authoritative sources.
  • Distinguish fear of change from evidence that the option is genuinely risky.

What a useful result looks like

The result should reveal which tradeoffs drive the recommendation and which missing facts are worth investigating before accepting or declining.