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Roundtable
The LLM Council, Built for Production

Your AI council is ready to deliberate

Roundtable is Karpathy's LLM council concept made real. Multiple AI models deliberate, challenge, and synthesize — not just compare. Research-validated at ICML 2024, built for production decisions.

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The Concept

What Is an LLM Council?

An LLM council queries multiple AI models on the same question and compares or deliberates over their responses. The concept originated from Andrej Karpathy's idea: instead of trusting one model's answer, assemble a council of models that each bring different training, different strengths, and different blind spots.

Diverse Perspectives

Different models have different training data, different strengths, and different blind spots. Querying multiple models surfaces perspectives no single model would produce.

Adversarial Challenge

Models read and challenge each other's reasoning, exposing weak arguments, unsupported claims, and confirmation bias before they reach your decision.

Synthesized Verdict

A Council Moderator reads all positions and produces a structured synthesis — consensus points, disagreements, trade-offs, and a final verdict.

The Gap

One Model Gives You an Answer. A Council Gives You the Debate.

Asking ChatGPT is like consulting one expert. An LLM council assembles a panel of experts who challenge each other's reasoning — so the blind spots get caught before you commit.

Current

Ask One Model

ChatGPT, Claude, or Gemini. One perspective, one answer.

The Upgrade

Council Deliberation

Multiple models debate. Cross-examine. Synthesize.

Outcome

Informed Decision

Consensus, dissent, trade-offs — all documented.

Every important decision has trade-offs. A council makes them visible. Architecture decisions, investment theses, research validation — any question that deserves more than one model's opinion.

Who This Is For

Built for Anyone Making High-Stakes Decisions

If your decision has genuine trade-offs, an LLM council surfaces the perspectives you'd miss with a single model. The more ambiguous the question, the more valuable the deliberation.

Researchers & Analysts

You need to validate findings across multiple perspectives before publishing. A single model gives you one interpretation — a council gives you the debate your reviewers would.

Single-perspective research validation

Engineering Leads

Architecture decisions, build-vs-buy, technology selection — every decision has trade-offs that one model glosses over. Your council surfaces the arguments before they become production incidents.

Architecture decisions with hidden trade-offs

Product Teams

Feature prioritization, market positioning, pricing strategy. When you ask one AI, you get one opinion dressed as a recommendation. A council gives you the full debate.

Strategic decisions that need multiple viewpoints

Investment Analysts

Due diligence, risk assessment, market analysis. You need adversarial challenge, not agreement. Your council plays bull case vs bear case so you don't have to guess which one GPT was trained on.

Investment decisions needing adversarial analysis
The Single-Model Problem

Why One Model Isn't Enough for Important Decisions

A single LLM produces one confident answer from one perspective. For questions with genuine trade-offs, that's not analysis — it's a coin flip with better grammar.

01

Echo Chamber of One

A single model has one training distribution, one set of biases, and one perspective. It produces one confident answer and has no mechanism to challenge itself. LLM councils break this by forcing multiple models with different training data to argue the same question.

02

No Cross-Verification

When you ask ChatGPT a question, there's no second model fact-checking the response. Hallucinations, fabricated citations, and unsupported claims go unchallenged. In a council, every claim gets tested by models with different knowledge bases.

03

Single-Dimension Reasoning

Complex decisions involve security, performance, cost, compliance, and team dynamics simultaneously. A single model produces one coherent narrative but misses the tensions between dimensions. Council deliberation surfaces these tensions explicitly.

An LLM council fixes this. When a Systems Architect and Security Reviewer debate the same design — and a Pragmatist grounds everything in operational reality — confirmation bias gets caught, blind spots get flagged, and the trade-offs become visible.

Why Deliberation Beats Consensus

Side-by-side display shows answers. Deliberation forces engagement. That's the difference between a comparison tool and a council.

Echo Chambers Break

When models must respond to disagreement, confirmation bias collapses. No more "yes-and" responses — every claim gets tested.

Hallucinations Get Caught

Cross-verification between models catches fabricated citations, incorrect facts, and unsupported claims before they reach your decision.

Reasoning Sharpens

Structured debate forces models to defend positions with evidence. Weak arguments don't survive adversarial pressure.

+28 percentage points

Multi-model debate improves accuracy by +28 percentage points — ICML 2024 Best Paper (Khan et al.)

Configurable Roles

Assign Roles. Start the Deliberation.

In Roundtable, you pick the AI models and assign each one a role \u2014 just like assembling a real advisory council. Here's a setup teams use for architecture decisions:

Systems Architect

Claude

System design, service boundaries, data flow, and long-term architectural sustainability. Evaluates structural trade-offs.

Scalability Engineer

GPT-4

Latency analysis, throughput modeling, resource optimization, and scalability assessment under production load.

Security Reviewer

Gemini

Attack surface analysis, compliance implications, data protection boundaries, and authentication architecture.

Pragmatist

Grok

Operational complexity, team capacity, timeline constraints, migration risk, and real-world deployment feasibility.

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Architecture Review Council

ANSystems Architect
OPPerformance Engineer
GOCritic
XABuilder

Systems design, security assessment, performance analysis, and operational readiness for architecture decisions.

Strategic Decision Council

ANBusiness Analyst
OPRisk Assessor
GOIdeator
XABuilder

Business analysis, risk assessment, innovation evaluation, and operational impact for strategic decisions.

Code Review Council

ANSenior Engineer
OPSecurity Auditor
GOPerformance Specialist

Senior engineering review, security auditing, performance analysis, and API design evaluation.

Product Decision Council

ANProduct Manager
OPUX Researcher
GOBuilder
XAAnalyst

Product strategy, user research, technical feasibility, and data-driven decision making.

Deliberation Modes

Four Ways to Structure the Debate

Debating

Models surface genuine disagreements and explain why they see things differently.

Analyzing

Models examine from different angles, challenging each other's framings.

Brainstorming

Models spark off each other's ideas, building and branching in real-time.

Problem Solving

Models build on each other's proposals toward actionable recommendations.

You choose the mode. The models do the rest.

One Model's Opinion \u2192 Council-Grade Deliberation

From guessing which model to trust to having all perspectives synthesized.

Single ModelOne Perspective
  1. 1Open ChatGPT. Ask your question. Get one answer.
  2. 2Try a different model. Get a different answer.
  3. 3Compare manually. No cross-examination, no synthesis.
  4. 4Make a decision based on whichever answer sounded most convincing.
LLM CouncilFull Deliberation
  1. 1Assemble your council — pick models and assign roles
  2. 2Models deliberate sequentially, reading and challenging each other
  3. 3Council Moderator synthesizes consensus, dissent, and trade-offs
  4. 4You make the decision with the full debate in front of you
Research Validation

The Science Behind LLM Councils

The research is clear: AI models produce better answers when they argue. Three landmark studies establish why council-style deliberation outperforms single-model queries.

+28pp

accuracy improvement via multi-model adversarial debate

Khan et al., ICML 2024 Best Paper

> GPT-4o

open-source models collaborating outperform GPT-4 Omni

Wang et al., ICLR 2025

70→95%

factual accuracy improvement in benchmark evaluations

Du et al., 2023

"Structured disagreement catches trade-offs, risks, and edge cases that no single model surfaces on its own."
ICML 2024 Best PaperNeurIPS 2024ICLR 2025

Multi-model debate improves accuracy by +28 percentage points — validated at ICML 2024 (Best Paper), NeurIPS 2024, and ICLR 2025.

Read the research

How Roundtable Compares to Other Council Tools

Roundtable

  • Sequential deliberation
  • Structured modes
  • Role-based personas
  • Moderator synthesis
  • MCP integration
  • Research-validated

Raw LLM Council

  • Sequential deliberation
  • Structured modes
  • Role-based personas
  • Moderator synthesis
  • MCP integration
  • Research-validated

ChatHub / TypingMind

  • Sequential deliberation
  • Structured modes
  • Role-based personas
  • Moderator synthesis
  • MCP integration
  • Research-validated

Council AI

  • Sequential deliberation
  • Structured modes
  • Role-based personas
  • Moderator synthesis
  • MCP integration
  • Research-validated
FAQ

Frequently asked questions

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Built for Every High-Stakes Decision

Start Your First Council Debate

Assemble your AI council. Pick the models. Choose the mode. Whether it's architecture decisions, investment analysis, or any question that deserves more than one perspective \u2014 your council is ready.

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