Tutorial: Optimizing LLM Council Performance for Speed and Accuracy
Learn advanced techniques to optimize your LLM council for faster responses and higher accuracy.
LLM counciloptimizationAI performancemulti-model AIcouncil tuning
Performance Optimization Goals
A well-optimized LLM council balances three factors: accuracy, speed, and cost. This tutorial shows you how to tune each.
Speed Optimization
Parallel Execution
Ensure models run simultaneously, not sequentially:
- SPRAPP fans out queries by default
- Parallel calls reduce latency to ~2x single model time
- Sequential calls multiply latency
Model Selection for Speed
Fast models for quick consensus:
- GPT-4o-mini: Ultra-fast, good quality
- Gemini 1.5 Flash: Speed champion
- Claude 3.5 Haiku: Fast with quality
Caching Strategies
Enable response caching for:
- Repeated queries
- Similar questions
- Static information requests
Accuracy Optimization
Model Diversity
Include models with different strengths:
- Different providers (Anthropic, OpenAI, Google, xAI)
- Different architectures (dense, MoE)
- Different training data (Western, Chinese)
Consensus Thresholds
Adjust based on stakes:
- High stakes: Require 4/5 agreement
- Medium stakes: Require 3/5 agreement
- Low stakes: Accept 2/3 majority
Peer Review Mode
Enable for complex queries:
- Models critique each other
- Catches errors majority misses
- Adds 30-50% latency but improves accuracy
Cost Optimization
Tiered Councils
Use different council sizes:
- Quick queries: 2 cheap models
- Standard queries: 3 mixed models
- Critical queries: 5 premium models
Smart Routing
Route queries intelligently:
- Coding → GLM-5, DeepSeek Coder
- Current events → Grok
- Long documents → Gemini
- General → GPT-4o, Claude
Token Management
- Set max output tokens
- Use streaming for long responses
- Implement truncation for inputs
Monitoring Dashboard
Track these metrics:
- Average response time
- Consensus rate
- Cost per query
- Accuracy spot-checks
Optimization Checklist
- Enable parallel execution
- Configure caching
- Set appropriate consensus threshold
- Implement smart routing
- Monitor and iterate
Optimized councils can achieve 90%+ accuracy while keeping costs reasonable.