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

Run your first AI council

A practical walkthrough from entering a question to reading the synthesis and token receipt.

The basic idea

The fastest way to understand AI Deliberator is to run one bounded question, inspect where the models disagreed, and then refine the question with a follow-up. The visible transcript matters as much as the final memo.

Example

Try a reversible decision with several real constraints, such as whether to apply for a leadership role or begin a certificate program.

How to do it

  1. 1

    Open Ask the council and either use a saved experiment or begin a quick decision.

  2. 2

    Enter the decision and the facts that materially constrain it.

  3. 3

    Choose the roster and rounds, then review the estimated credits. The run proceeds without another approval when the balance covers the estimate.

  4. 4

    Watch positions arrive, then read the synthesis, Consideration Map, token usage, provider cost, and customer charge.

What to watch for

  • A consensus can still be wrong; verify important factual claims.
  • If a model fails, read the failure policy before treating the remaining responses as a complete panel.
  • Do not use the service as emergency or professional advice.

What a useful result looks like

Your first run should leave you with a recommendation, visible disagreement, concrete follow-up questions, and an exact usage receipt.