In brief
- Generate candidates independently before sharing outputs.
- Judge claims and evidence, not confidence of tone.
- Synthesis must retain unresolved disagreement.
Model plurality is not source plurality
Different models can repeat the same widely circulated error or rely on similar training material. Multiple answers help explore hypotheses, but factual confidence must come from external evidence and verification.
Consensus among models is a signal to inspect—not proof that a claim is true.
Use an evidence-first council workflow
Ask candidates to answer independently with claim-level sources, then normalize their outputs for blind review. Have a judge compare support, omissions, and contradictions before synthesis.
- Independent candidate generation
- Source retrieval and validation
- Blind claim comparison
- Adjudication of conflicts
- Synthesis with uncertainty
Judge the evidence chain
Score source relevance, primary-versus-secondary evidence, freshness, whether the source entails the claim, and whether important counterevidence was addressed.
Synthesize without erasing uncertainty
Separate supported findings, plausible interpretations, disputed points, and unanswered questions. Link the final answer to the candidate and source artifacts so a reader can audit the path.
Frequently asked
Questions, answered plainly.
Do more AI models make an answer more accurate?+
Not automatically. They can increase coverage and expose disagreement, but accuracy still depends on source quality, verification, and evaluation.
Should models see each other's answers?+
Generate independent first passes before cross-critique to reduce anchoring and preserve different approaches.
What should the final synthesis include?+
Supported claims, source links, important disagreements, uncertainty, and the checks still required.
Sources and next paths