Aa Benchmarking Framework

Composite scoring and efficiency frontier analysis for LLM evaluation — combines multiple quality dimensions (accuracy, latency, cost, consistency) into a single Pareto-optimal ranking. Use when comparing models or agent configurations across competing objectives, building evaluation dashboards, or identifying the efficiency frontier for model selection. Implements weighted composite scores, Pareto frontier detection, and radar chart visualisation for multi-dimensional LLM benchmarking.

Last used: 2026-03-24 Memory references: 1 Status: Active

STATUS: DRAFT — This skill is planned but not yet fully implemented.

What This Does

Provides a systematic framework for multi-dimensional LLM evaluation using composite scoring, efficiency frontier analysis, and Pareto optimality. Rather than ranking models on a single metric, it helps identify which models are non-dominated — i.e., no other model is better on all dimensions simultaneously. Designed for teams that need principled model selection beyond simple leaderboard rankings.

Planned Capabilities

• Composite scoring with configurable dimension weights (accuracy, latency, cost, recall, F1)
• Pareto frontier detection across any two or more evaluation dimensions
• Radar/spider chart visualisation for multi-dimensional comparison
• Statistical significance testing across benchmark runs (t-test, Mann-Whitney U)
• Integration with LangFuse for trace-based evaluation data ingestion
• Export to CSV/JSON for downstream analysis

When To Use

• Choosing between 3+ LLM providers on competing objectives (e.g. GPT-4o vs Claude 3.5 vs Gemini)
• Building an evaluation dashboard for recurring model benchmarks
• Presenting model selection rationale to stakeholders with visual evidence
• Running efficiency frontier analysis to identify cost-optimal models for a quality threshold