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 live evidence mode

Ask the Council to retrieve current sources before deliberating and preserve the source list with the run.

The basic idea

Evidence mode reduces reliance on stale recall by supplying a shared retrieved evidence pack. A returned URL confirms provenance, not truth: users must still assess source authority, relevance, and interpretation.

Example

For a college-admissions decision, retrieve current official admissions, program, and financial-aid pages before the advisers exchange views.

How to do it

  1. 1

    Enable Use live evidence search in run controls.

  2. 2

    Review the higher free estimate and confirm the balance can cover it.

  3. 3

    Inspect the retrieved brief and links alongside the Council answer.

  4. 4

    Open consequential sources and verify the specific claims before acting.

What to watch for

  • Search can miss sources or retrieve a page that does not support the model's interpretation.
  • Only displayed retrieved URLs should be treated as source references.
  • Evidence mode sends the question to the search/model provider and adds a metered call.

What a useful result looks like

The run retains an auditable source list and a grounded Council response while clearly preserving epistemic limits.