Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems
#LLM-as-a-Judge #RAG evaluation #Enterprise systems #Multi-turn workflows #Case-aware evaluation #Technical support #AI assessment #Deterministic prompting
📌 Key Takeaways
- Researchers developed a case-aware LLM-as-a-Judge evaluation framework for enterprise RAG systems
- The framework uses eight operationally grounded metrics to evaluate multi-turn interactions
- Existing evaluation methods fail to capture enterprise-specific failure modes in complex workflows
- The new system enables scalable batch evaluation and production monitoring through deterministic prompting
📖 Full Retelling
Researchers Mukul Chhabra, Luigi Medrano, and Arush Verma introduced a case-aware LLM-as-a-Judge evaluation framework for enterprise-scale Retrieval-Augmented Generation systems on February 23, 2026, through a paper published on arXiv, addressing critical gaps in existing evaluation methods that fail to capture enterprise-specific failure modes in multi-turn workflows. The paper highlights that enterprise RAG assistants operate in complex, case-based workflows like technical support and IT operations, where evaluation must consider operational constraints, structured identifiers (such as error codes and versions), and resolution workflows. The existing frameworks are primarily designed for simpler, single-turn scenarios, making them inadequate for enterprise environments where cases span multiple interactions and require specific workflows.
The proposed framework evaluates each interaction using eight operationally grounded metrics that separate retrieval quality, grounding fidelity, answer utility, precision integrity, and case/workflow alignment. Additionally, a severity-aware scoring protocol reduces score inflation and improves diagnostic clarity across different enterprise cases. The system employs deterministic prompting with strict JSON outputs, enabling scalable batch evaluation, regression testing, and production monitoring in enterprise settings.
Through a comparative study of two instruction-tuned models across both short and long workflows, the researchers demonstrated that generic proxy metrics provide ambiguous signals, while their framework exposes enterprise-critical tradeoffs that are actionable for system improvement. This work represents a significant advancement in evaluating complex AI systems in enterprise environments, where accurate assessment is crucial for deployment and optimization, particularly in high-stakes technical support and IT operations scenarios.
🏷️ Themes
AI Evaluation, Enterprise Systems, Retrieval-Augmented Generation
📚 Related People & Topics
Technical support
Maintenance service of electronic consumers
Technical support, commonly shortened as tech support, is a form of customer service provided to assist users in resolving problems with products such as consumer electronics and software. Technical support is typically delivered through call centers, online chat, and email services. In addition, ma...
Enterprise software
Software targeted towards corporations/organisations
Enterprise software, also known as enterprise application software (EAS), is computer software that has been specially developed or adapted to meet the complex requirements of larger organizations. Enterprise software is an integral part of a computer-based information system, handling a number of b...
Entity Intersection Graph
No entity connections available yet for this article.
Original Source
--> Computer Science > Computation and Language arXiv:2602.20379 [Submitted on 23 Feb 2026] Title: Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems Authors: Mukul Chhabra , Luigi Medrano , Arush Verma View a PDF of the paper titled Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems, by Mukul Chhabra and 2 other authors View PDF HTML Abstract: Enterprise Retrieval-Augmented Generation assistants operate in multi-turn, case-based workflows such as technical support and IT operations, where evaluation must reflect operational constraints, structured identifiers (e.g., error codes, versions), and resolution workflows. Existing RAG evaluation frameworks are primarily designed for benchmark-style or single-turn settings and often fail to capture enterprise-specific failure modes such as case misidentification, workflow misalignment, and partial resolution across turns. We present a case-aware LLM-as-a-Judge evaluation framework for enterprise multi-turn RAG systems. The framework evaluates each turn using eight operationally grounded metrics that separate retrieval quality, grounding fidelity, answer utility, precision integrity, and case/workflow alignment. A severity-aware scoring protocol reduces score inflation and improves diagnostic clarity across heterogeneous enterprise cases. The system uses deterministic prompting with strict JSON outputs, enabling scalable batch evaluation, regression testing, and production monitoring. Through a comparative study of two instruction-tuned models across short and long workflows, we show that generic proxy metrics provide ambiguous signals, while the proposed framework exposes enterprise-critical tradeoffs that are actionable for system improvement. Comments: 12 pages including appendix, 6 figures Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20379 [cs.CL] (or arXiv:2602.20379v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2602....
Read full article at source