A Modular Framework for Business-Specific Benchmarking of Large Language Models

Abstract

Large Language Models (LLMs) are being rapidly integrated into core business functions, creating a need for evaluation methods that measure performance against specific operational goals rather than generic metrics that are typically used for evaluating them. The standard approaches are developing fully custom benchmarks or adapting standardized frameworks like HELM. We propose a structured, hybrid methodology that integrates a robust LLM-as-a-Judge (LJ) pipeline into a business-aligned testing strategy. The framework consists of a comprehensive six-stage methodology for designing, creating, and maintaining business-relevant benchmarks; a modular reference architecture that operationalizes its work, automating evaluation to minimize human involvement; a practical validation of the framework through a case study comparing multiple LLMs on a financial advisory task. The results demonstrate that this approach not only provides a more accurate assessment of model's fitness for a specific business purpose but also integrates evaluation into the Machine Learning Operations (MLOps) lifecycle, enabling continuous improvement and management.

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