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

Participant studies

Monitor recruitment, cost, and attrition

Use the experiment activity dashboard to detect drop-off, failed calls, retention loss, and budget pressure while a study is live.

The basic idea

Operational monitoring protects both participants and inference. Completion, treatment exposure, failures, and cost per completion should be examined together without changing a locked protocol in response to interim outcomes.

Example

Watch whether participants reach the pre-measure, treatment, questionnaire, and delayed assessment while monitoring failed model calls and remaining owner credits.

How to do it

  1. 1

    Open the experiment and review Researcher activity.

  2. 2

    Compare starts, exposures, completions, and the largest recorded drop-off stage.

  3. 3

    Check failures, active runs, provider cost, cost per completion, retention returns, and reminder delivery.

  4. 4

    Pause recruitment if a technical or funding problem could create systematically missing treatment data.

What to watch for

  • The largest drop-off label is descriptive, not a causal diagnosis.
  • Do not repeatedly inspect treatment outcomes unless the analysis plan permits it.
  • Collaborators can see experiment operations; only the owner pays.

What a useful result looks like

A compact operational record shows whether recruitment is producing usable, fully exposed cases at an acceptable cost.