Human-LLM Collaboration for Reliable Prompt Monitoring and Benchmarking of Domain-Specific Language Assistants

This paper proposes a hybrid evaluation framework for domain-specific language assistants, combining prompt-triggered automated benchmarking with targeted human-in-the-loop validation. Classic metrics such as BLEU fail to capture the complexity of structured entity extraction and semantic variability required in operational workflows. By integrating prompt monitoring, the system ensures that benchmarking is triggered only by meaningful changes, maintaining alignment with evolving prompt structures. Semantic evaluation leverages Large Language Models (LLMs) to match entity fields across domains, with comprehensive experiments confirming robust performance for translations and typographical variants. Weaknesses remain in distinguishing semantically close synonyms and certain typo types, highlighting the need for human oversight. The results demonstrate that Human-LLM collaboration enables accurate, context-sensitive monitoring and evaluation, providing a reproducible and scalable quality assurance strategy for real-world language assistant deployments.