Benchmark RadarEnterpriseRAG
The benchmark evaluates LLM instruction adherence and robustness in enterprise retrieval scenarios, using 983 expert-validated samples across six domains, simulating retrieval noise, knowledge gaps, and factual conflicts.
- Released
- 2026-08-12
- Readiness
- Paper only
- Primary field
- General AI
Why it matters
Existing RAG benchmarks assume clean retrieval and simple queries, failing to capture production conditions. This benchmark addresses the gap by measuring holistic compliance under non-ideal conditions, informing deployment decisions for enterprise-scale RAG systems.
Motivation
Enterprise RAG deployments face a critical reliability gap: while LLMs satisfy 80% of individual constraints, only 26.8% of responses meet all requirements simultaneously, revealing a 57-point orchestration gap.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.