Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems
arXiv:2602.20379v1 Announce Type: new Abstract: Enterprise Retrieval-Augmented Generation (RAG) 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 enterp
arXiv:2602.20379v1 Announce Type: new Abstract: Enterprise Retrieval-Augmented Generation (RAG) 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.
Executive Summary
The case-aware LLM-as-a-Judge evaluation framework presented in this article offers a comprehensive evaluation approach for enterprise-scale RAG systems, addressing current limitations of existing frameworks. By incorporating operationally grounded metrics and a severity-aware scoring protocol, the framework provides a more nuanced understanding of RAG performance. The study's comparative analysis highlights the importance of considering enterprise-specific failure modes and tradeoffs in system improvement. The framework's scalability and deterministic prompting enable batch evaluation, regression testing, and production monitoring, making it a valuable tool for RAG system development and optimization.
Key Points
- ▸ The framework addresses enterprise-specific evaluation challenges in multi-turn, case-based workflows.
- ▸ It incorporates eight operationally grounded metrics to evaluate RAG performance.
- ▸ The severity-aware scoring protocol reduces score inflation and improves diagnostic clarity.
Merits
Comprehensive Evaluation Approach
The framework provides a thorough evaluation of RAG systems, addressing various aspects of performance, including retrieval quality, grounding fidelity, and case/workflow alignment.
Scalability and Deterministic Prompting
The framework's scalability and deterministic prompting enable batch evaluation, regression testing, and production monitoring, making it a valuable tool for RAG system development and optimization.
Demerits
Limited Domain-Specific Evaluation
While the framework addresses enterprise-specific evaluation challenges, it may not be directly applicable to other domains or use cases, requiring further adaptation and evaluation.
Dependence on Instruction-Tuned Models
The framework's performance and effectiveness rely on the quality and characteristics of instruction-tuned models, which may not be universally applicable or available.
Expert Commentary
While the case-aware LLM-as-a-Judge evaluation framework addresses significant limitations in current evaluation approaches, its practical application and scalability are dependent on the quality and characteristics of instruction-tuned models. Further research is needed to explore the framework's generalizability and adaptability to various domains and use cases. Additionally, the framework's evaluation approach and metrics can be applied to other conversational AI systems, offering a valuable tool for assessing their performance and effectiveness.
Recommendations
- ✓ Further research should focus on exploring the framework's generalizability and adaptability to various domains and use cases.
- ✓ The framework's evaluation approach and metrics should be applied to other conversational AI systems, such as chatbots and virtual assistants, to assess their performance and effectiveness.
