Building an Effective LLM Council: Configuration Best Practices
Learn how to configure your LLM council for maximum accuracy, speed, and cost efficiency with proven strategies.
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Council Architecture Decisions
Building an effective LLM council requires thoughtful configuration. Here's how to optimize for different goals.
Model Selection
For General Purpose
- Claude 3.5 Sonnet (reasoning)
- GPT-4o (breadth)
- Gemini 1.5 Pro (context)
- Grok 2 (real-time)
For Coding Tasks
- Claude 3.5 Sonnet (code generation)
- GPT-4o (debugging)
- GLM-5 (SWE-bench performance)
- Nanbeige4.1-3B (efficient)
- DeepSeek Coder (specialized)
For Research
- Claude 3.5 Sonnet (depth)
- GPT-4o (breadth)
- Gemini 1.5 Pro (long context)
- GLM-4.6V (visual reasoning)
Consensus Configuration
High Stakes (Legal, Medical)
- 5+ models in fan-out
- Full debate mode
- Mandatory peer review
- Low consensus threshold for human review
Speed Priority
- 3 models maximum
- Smart Router mode
- No peer review
- Accept majority consensus
Cost Optimization
- 2 smaller models + 1 large
- Mixture of Agents
- Only trigger peer review on disagreement
Monitoring and Iteration
Track your council's performance:
- Consensus rate by query type
- Time to consensus
- Cost per query
- Accuracy spot-checks
Adjust configurations based on real performance data.