Title: Evaluation Frameworks for Business-Oriented LLM Systems Based on Acceptance-Test-Driven Protocols

Abstract: As Large Language Model (LLM) applications face growing demands to meet strict, deterministic institutional standards despite relying on probabilistic generative engines, traditional post-hoc benchmarking has proven inadequate. This insufficiency is particularly critical for systems requiring safety, reliability, auditability, and economic utility. To address this gap, this paper introduces an extension to evaluation protocols for operational LLM systems, synthesizing principles from acceptance-test-driven development, safety engineering, and business-centric validation.

This proposed framework converts stakeholder objectives into actionable components, including executable behavioral contracts, release gates, monitoring indicators, and evidentiary artifacts. These elements are established prior to the acceptance of any modifications to prompts, models, retrieval mechanisms, or agents. The approach adapts the classic "red-green-refactor" cycle of test-driven development into a "red-train-green" lifecycle. This process begins by defining acceptance tests that initially fail against desired behaviors. Subsequently, the LLM system is refined through interventions such as prompt engineering, retrieval architecture adjustments, fine-tuning, the implementation of guardrails, or data augmentation. Release is authorized only when all multidimensional criteria are met.

The primary contributions of this work include a governance-focused metric stack, a reference architecture, and an empirical protocol. These tools facilitate a comparative analysis of acceptance-test-driven LLM development against conventional prompt-first and benchmark-after methodologies.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC