Last used: 2026-03-24

Memory references: 1

Status: Active

AA Benchmarking Framework

> 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