Tutorial: Troubleshooting Common LLM Council Issues
Solve the most common problems when setting up and running LLM councils, from API errors to consensus failures.
Common Issues and Solutions
Even well-configured LLM councils encounter problems. Here's how to diagnose and fix them.
Issue 1: Low Consensus Rate
Symptoms: Models frequently disagree, consensus rarely achieved.
Causes:
- Ambiguous queries
- Models with similar weaknesses
- Temperature too high
Solutions:
- Clarify your questions
- Add model diversity
- Lower temperature to 0.3-0.5
- Enable peer review mode
Issue 2: High Latency
Symptoms: Queries take 30+ seconds.
Causes:
- Too many models
- Sequential execution
- Large token outputs
- Network issues
Solutions:
- Reduce council size to 3-4 models
- Verify parallel execution
- Set max_tokens limits
- Check API provider status
Issue 3: API Errors
Symptoms: 401, 429, 500 errors.
Causes:
- Invalid API keys
- Rate limiting
- Provider outages
Solutions:
- Verify API keys in settings
- Check usage limits
- Implement retry logic
- Use fallback providers
Issue 4: Expensive Queries
Symptoms: Costs higher than expected.
Causes:
- Too many premium models
- Long outputs
- No caching
Solutions:
- Mix premium and budget models
- Limit output tokens
- Enable response caching
- Use smart routing
Issue 5: Inconsistent Quality
Symptoms: Good answers sometimes, poor others.
Causes:
- Random model selection
- No quality filtering
- Inappropriate consensus mode
Solutions:
- Fix model selection
- Add confidence thresholds
- Use appropriate consensus mode
- Implement quality checks
Issue 6: Hallucinations
Symptoms: Models produce false information confidently.
Causes:
- Insufficient model diversity
- No fact-checking
- Domain gaps
Solutions:
- Add fact-checking models (Grok, Perplexity)
- Enable peer review
- Increase consensus threshold
- Add verification step
Debugging Tools
Response Logs
Review individual model responses to identify weak links.
Confidence Scores
Low confidence indicates uncertainty—investigate those queries.
Consensus Analysis
See which models agree/disagree to understand patterns.
Prevention Checklist
- Diverse model selection
- Appropriate consensus threshold
- Response caching enabled
- Error handling implemented
- Monitoring in place
- Regular performance reviews
Most issues resolve with proper configuration and monitoring.