Acceptance-Test-Driven Evaluation Protocols for Business-Centric LLM Systems
Eric Liang
Why It Matters
What makes this one worth your time
This approach could improve the reliability and safety of LLM systems in business contexts by ensuring they meet specific stakeholder requirements before deployment.
The paper introduces a governance-oriented evaluation protocol for LLM systems using acceptance-test-driven development.
Summary
The paper proposes an evaluation protocol for large language model systems that integrates acceptance-test-driven development with safety engineering and business-centric validation to ensure systems meet deterministic requirements despite their probabilistic nature.
Key contributions
- Development of a governance-oriented metric stack for LLM evaluation.
- Proposal of a reference architecture for acceptance-test-driven LLM systems.
- Introduction of an empirical protocol for comparing different LLM development workflows.
Notable insights
- Adapts test-driven development principles to LLM evaluation, emphasizing stakeholder-driven behavioral contracts.
- Introduces a red-train-green lifecycle for iterative system improvement and validation.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.02755v1 Announce Type: cross Abstract: Large language model (LLM) applications are increasingly expected to satisfy deterministic institutional requirements while relying on probabilistic generative components. This mismatch makes ordinary post-hoc benchmarking insufficient for systems that must be safe, reliable, auditable, and economically useful. This paper contributes an evaluation-protocol extension for operational LLM systems grounded in acceptance-test-driven development, safety engineering, and business-centric validation. The extension translates stakeholder goals into executable behavioral contracts, release gates, monitoring signals, and evidence artifacts before prompt, model, retrieval, or agent changes are accepted. It adapts the red-green-refactor discipline of test-driven development to a red-train-green lifecycle: first define failing acceptance tests for desired behavior, then improve the LLM system through prompt changes, retrieval design, fine-tuning, guardrails, or data augmentation, and finally release only when multidimensional gates are satisfied. The contribution is a governance-oriented metric stack, reference architecture, and empirical protocol for comparing acceptance-test-driven LLM development against prompt-first and benchmark-after workflows.