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
AA 基准测试框架
状态：草稿 — 该技能已规划但尚未完全实现。
功能说明
提供一套系统化的框架，用于通过综合评分、效率前沿分析和帕累托最优性进行多维度大语言模型评估。该框架并非基于单一指标对模型进行排名，而是帮助识别哪些模型是非支配的——即没有其他模型能在所有维度上同时表现更优。专为需要超越简单排行榜排名、进行有原则的模型选择的团队设计。
规划能力
- - 支持可配置维度权重的综合评分（准确率、延迟、成本、召回率、F1值）
- 任意两个或多个评估维度的帕累托前沿检测
- 用于多维度比较的雷达/蜘蛛图可视化
- 跨基准测试运行的统计显著性检验（t检验、曼-惠特尼U检验）
- 与LangFuse集成，用于基于追踪的评估数据摄取
- 导出为CSV/JSON格式，供下游分析使用
使用场景
- - 在竞争性目标下选择3个以上大语言模型提供商（如GPT-4o对比Claude 3.5对比Gemini）
- 为定期模型基准测试构建评估仪表板
- 向利益相关者展示模型选择依据，并提供可视化证据
- 运行效率前沿分析，以确定满足质量阈值的最优成本模型
