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 exec
As LLM systems move from research to critical enterprise applications, the need for robust, auditable evaluation methods becomes paramount to ensure reliability and safety.
This development addresses a key bottleneck for institutional adoption of LLMs by introducing a formal, business-centric validation framework, directly impacting trust and deployment at scale.
The shift from ad-hoc benchmarking to structured, acceptance-test-driven evaluation protocols for LLMs will enable their integration into regulated and mission-critical environments.
- · Enterprises adopting LLMs
- · LLM developers building for regulated industries
- · AI safety and auditing firms
- · Software engineering consultancies
- · LLM providers with poor evaluation practices
- · Companies relying solely on post-hoc benchmarking
- · Traditional software testing firms without AI expertise
Increased enterprise adoption of LLM systems due to improved reliability and auditability.
Development of new tooling and services specifically for LLM acceptance testing and validation.
Potential for regulatory bodies to mandate similar evaluation protocols for AI systems in critical infrastructure.
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Read at arXiv cs.AI