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

Evaluate and export a Council run

Rate a completed decision, record how it affected your thinking, and download an auditable report.

The basic idea

A useful Council product should be evaluated as a decision aid, not only admired as fluent text. The private post-run rating captures perceived usefulness and decision effect, while the export preserves the reasoning and configuration needed to inspect the run later.

Example

After a major education decision, rate the run five out of five, record that it confirmed the plan while adding perspectives, and download the Markdown report for a family discussion.

How to do it

  1. 1

    Complete a Council run and wait for its results to be released.

  2. 2

    Choose a usefulness score and the statement that best describes its effect on your thinking; optionally add confidence and a private note.

  3. 3

    Save the rating. You can revise your response later from the same run.

  4. 4

    Download Markdown for a readable record or JSON for structured analysis. Experiment owners can export de-identified feedback summaries separately.

What to watch for

  • A usefulness rating is not a factual-accuracy score.
  • Reports can contain sensitive prompt and transcript content, so store or share them deliberately.
  • Payment-held results cannot be exported until the balance is settled.

What a useful result looks like

You retain a portable transcript, Consideration Map, usage receipt, and reproducibility manifest while building evidence about which Council designs users find valuable.