Multi-model peer review layer using local LLMs via Ollama to catch errors in cloud model output.
Fan-out critiques to 2-3 local models, aggregate flags, synthesize consensus.
Use when: validating trade analyses, reviewing agent output quality, testing local model accuracy,
checking any high-stakes Claude output before publishing or acting on it.
Don't use when: simple fact-checking (just search the web), tasks that don't benefit from
multi-model consensus, time-critical decisions where 60s latency is unacceptable,
reviewing trivial or low-stakes content.
Negative examples:
- "Check if this date is correct" → No. Just web search it.
- "Review my grocery list" → No. Not worth multi-model inference.
- "I need this answer in 5 seconds" → No. Peer review adds 30-60s latency.
Edge cases:
- Short text (<50 words) → Models may not find meaningful issues. Consider skipping.
- Highly technical domain → Local models may lack domain knowledge. Weight flags lower.
- Creative writing → Factual review doesn't apply well. Use only for logical consistency.
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