Benchmark Ecological Validity

Benchmarks built from industry corpora (user manuals, legal contracts, medical literature) predict real-world performance better than synthetic or academic-only datasets.

RAGBench deliberately sources from user manuals and domain-specific documents rather than clean academic Q&A pairs. The rationale: production RAG systems face messy, domain-specific content, not Wikipedia paragraphs.

The pattern I keep encountering: benchmarks optimized for clean performance often fail to predict messy deployment. A model that excels at answering questions about well-structured encyclopedia articles may struggle with verbose legal contracts or terse technical documentation.

The implication for benchmark design: include the noise, formatting quirks, and domain-specific vocabulary of actual deployment contexts. Clean data produces clean results that don’t transfer.

Related: 05-atom—demos-deployment-ethics-gap, 03-atom—benchmark-ecological-validity