Human-in-the-Loop Evaluation Pipelines for Production LLM Systems

Large Language Models (LLMs) have become essential tools in a wide variety of domains; however, inherent limitations such as hallucinations, ambiguities, domain-specific errors, and others often create significant problems when used in the context of productions. We present a Human-in-the-Loop (HITL) assessment pipeline in this work that is an integration of human feedback, LLM-based adjudication system, and observability tools and expect to increase the reliability, safety and alignment of the results produced by the LLM with the business needs. It implements the use of human raters to create evaluation rubrics, review model outputs and provide feedback, and an adjudicator of the LLM automates the scoring process to ensure scalability. Empirical case studies on the customer support and document quality assurance have shown that the hybrid approach brings improvements to the accuracy of task completion, customer satisfaction, and policy compliance. Moreover, the manuscript outlines the best practices regarding the scaling of the HITL workflows, handling high-risk cases, and continuously refining the system performance through the continuous feedback processes. In this way, the suggested solution will provide a scalabilitybased, low-cost paradigm to enrich the usage of LLM applications and, at the same time, maintain the alignment with the business requirements and safety level.