Adaptive Classification for Automatic LLM Temperature Optimization

After analyzing 500 diverse LLM queries (from Arena Auto Hard benchmark), we found that the typical temperature=0.7 setting is only optimal 23.5% of the time. We built an adaptive classification system that automatically predicts optimal temperature ranges based on query characteristics, achieving a 69.8% success rate in finding better configurations. Key findings: Query-Temperature Mapping: Factual queries (math, logic) → 0.0-0.1 Technical writing → 0.2-0.5 Conversational → 0.6-1.0 Creative tasks → 1.1-1.5 Exploratory thinking → 1.6-2.0 Distribution Analysis: Most queries actually benefit from non-standard temperatures: 26.4% perform best with technical precision (0.2-0.5) 23.5% with standard balanced settings (0.6-1.0) 18.6% with strict deterministic settings (0.0-0.1) 31.6% with higher creative settings (>1.0) Implementation: We use Round-Trip Consistency (RTC) testing: generate a response, create an alternate query targeting similar content, generate second response, measure semantic similarity. This provides an automated way to evaluate temperature effectiveness without human intervention. The classifier learns continuously from query-response patterns. In production, this reduced "hallucinations" by 42% for factual queries while improving creativity scores by 35% for open-ended tasks. Technical details and implementation: https://github.com/codelion/adaptive-classifier We're particularly interested in feedback from others who've dealt with temperature optimization at scale.