# Peer review that ranks answers—not model brands.

> LLM Peers anonymizes model answers and peer-ranks them against shared criteria so review focuses on substance, not brand preference.

Canonical HTML: https://llmpeers.com/features/peer-review

Stage 2 anonymizes council answers and asks models to evaluate them against the same criteria. The result is a comparative ranking and a clearer map of agreement and dissent—peer ranking as a deliberative step, not a popularity contest among model names.

## What anonymized peer review does

After the AI Council produces independent answers, LLM Peers strips model identity and runs a structured review. Reviewers score options against shared evaluation criteria so comparison stays on substance: strategic fit, risk, completeness, and decision usefulness—not which brand wrote the draft.

## Why peer ranking matters

A pile of chat answers still leaves you as the ranking engine. Peer review turns multi-model output into a deliberative process: options are compared, dissent stays visible, and prestige bias is reduced because reviewers do not know who wrote each answer during anonymized review.

## When peer review is most valuable

Lean on this stage when options look similarly plausible, when stakeholders disagree, or when you need an audit trail of why one path ranked above another. It is the bridge between independent research and a chairman decision brief you can defend in a meeting.

### Anonymized evaluation

Reviewers do not know which model wrote each answer, reducing prestige bias and keeping focus on the quality of the recommendation.

### Shared criteria

Options are scored against a consistent decision framework instead of incompatible vibes or one-off follow-up prompts.

### Visible disagreement

Partial agreement and key dissent remain visible so human judgment can weigh genuine uncertainty—not only the winning narrative.

### Ranking ready for synthesis

Peer ranking produces comparative signal the chairman can synthesize into a decision brief with ranked options and clear next action.

### Does peer review mean models grade their own work?

Models evaluate anonymized council answers against shared criteria. Identity is hidden during review so ranking reflects the substance of each option rather than loyalty to a model brand.

### What is the difference between peer ranking and averaging answers?

Averaging blends views and can hide trade-offs. Peer ranking compares distinct options, preserves dissent, and feeds a structured decision brief instead of a mushy consensus paragraph.

### Can I still disagree with the top-ranked answer?

Yes. Peer review informs judgment; it does not replace it. The decision brief surfaces recommendation, ranked options, and unresolved questions so you remain the decision maker.

### Related features
- [AI Council](https://llmpeers.com/features/ai-council): Where competing multi-model answers come from
- [Decision brief](https://llmpeers.com/features/decision-brief): Synthesis after anonymized peer ranking
- [Challenges with single AI](https://llmpeers.com/challenges): Why informal comparison falls short
- [Methodology](https://llmpeers.com/methodology): How deliberation stages fit together

## Related pages

- [Methodology](https://llmpeers.com/methodology)
- [Multi-agent vs single AI](https://llmpeers.com/multi-agent-vs-single-ai)
- [Benefits](https://llmpeers.com/benefits)
- [AI Council](https://llmpeers.com/features/ai-council)

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Publisher: Version Labs · Product: LLM Peers