CAFE: A Compound-AI Factorial Evaluation Framework
We introduce CAFE (Compound-AI Factorial Evaluation), an open-source platform that brings design of experiments to the evaluation of compound AI systems (CAIS). Such systems expose many interchangeable choices - e.g. which retriever, model, or prompt - and practitioners rarely know which of them most affects answer quality. With CAFE, a practitioner registers each swappable component of a pipeline as a factor to build a factorial design over the chosen factors, run the resulting configurations, and score the answers on a shared rubric using a configurable LLM judge together with human raters. From these ratings it attributes answer-quality variance to the components and their interactions...
| Authors | Fabian Lukassen, Christoph Weisser, Thomas Kneib et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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