Review one question with several AI models.
Choose models and a review format, then give them the same question and context. Read their arguments, conclusion, and unresolved objections in one place.
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A set of answers still needs a review process.
Separate model responses can be hard to reconcile. A review format sets out when models respond, what they should question, and how their views are brought together.
Use Delphi when you want independent opening views, or Devil’s Advocate when you want a recommendation challenged. Choose the format for the question rather than adding models for their own sake.
What to look for in the review.
Choose the participants
Select models from the available catalog and give them a clear task.
Compare their positions
Read what reviewers agree on and which assumptions they question.
Keep the reasoning
Inspect the transcript alongside the conclusion before acting on it.
LLM council vs. persona swarm vs. human committee
These approaches solve different problems. The useful comparison is not how many voices appear on screen, but how independence, challenge, accountability, and synthesis are handled.
| Decision dimension | Common alternative | RigorDesk council |
|---|---|---|
| Opening analysis | Personas often see the same prior answer | Protocols can protect independent first views |
| Opposition | Prompted politely and inconsistently | Named attacker, opposition, or examiner roles |
| Information flow | One accumulating chat history | Format-specific turn and visibility rules |
| Agreement | Majority vote or generic summary | Independent scoring plus pairwise points of disagreement |
| Final output | Blended response | Conclusion with dissent preserved |
| Accountability | Prompt history or meeting notes | Replayable transcript, costs, turns, and report |
Prepare a useful review.
Give reviewers a clear question and enough context to examine it.
Use Delphi for anchoring risk
When early opinions could contaminate later ones, begin with silent independent analysis.
Use Oxford Union for a real motion
If the decision can be stated as a clear proposition, create a proposition and opposition that must defend incompatible positions.
Do not confuse a larger council with independent evidence.
Frontier models can share public sources, evaluation habits, and culturally dominant assumptions. Five agreeing models may represent fewer than five independent information channels. Provider diversity, silent opening rounds, and adversarial roles reduce correlated reasoning, but they cannot eliminate it. Primary sources and accountable domain review still matter more than the headcount.
Council output should also match the stakes. Use it freely to improve a strategy memo or expose an architecture risk. For legal, medical, financial, employment, or safety decisions, treat the conclusion as an issue-spotting artifact for a qualified human. The transcript helps that reviewer see how the conclusion was tested; it does not transfer responsibility to the models.
What to know before you run the panel
How many models should an LLM council include?
Three models are a useful default for focused review; five can cover a broader set of assumptions when each has a defined role. RigorDesk supports panels from two to eight models. Add a member only when it contributes a distinct provider, capability, position, or review responsibility.
Can an LLM council reach a decision without forcing consensus?
Yes. The chairman can recommend a path while preserving a minority objection or naming a blocked decision. The goal is a usable conclusion with explicit uncertainty, not artificial unanimity. A 60% agreement score may be more informative than a smoothed 100% summary if the remaining objection changes the action.
Does agreement mean the answer is correct?
No. Models can repeat the same mistake or rely on the same weak source. Read the objections and check important claims against evidence.
Why use other models instead of asking one to critique itself?
Self-critique can help. Other models and specific review roles provide additional responses to compare. The format determines whether reviewers begin independently or see earlier answers.
Does this replace human review?
No. Use the findings to improve a draft or prepare questions for a responsible reviewer. The models do not approve a decision or verify every claim.
Bring your next draft.
Create a free account to draft or import your work. Choose the reviewers and settings before starting a review.