arXiv:2601.22025v2 Announce Type: replace Abstract: Evaluating Large Language Model (LLM) applications differs from conventional software testing because outputs are probabilistic, semantically variable, and sensitive to prompt and model changes. This technical report proposes the Minimum Viable Evaluation Suite (MVES), an audit-oriented structure for application-level LLM evaluation. MVES links application categories to failure modes, metrics, required artifacts, and validation evidence across general LLM applications, retrieval-augmented systems, and agentic workflows. We pair the framework
As LLM applications become more sophisticated and ubiquitous, the need for robust and reliable evaluation methods is critical to ensure their responsible deployment and efficacy.
A strategic reader needs to understand how to effectively evaluate the burgeoning field of LLM applications, as current testing methods are insufficient for their probabilistic and dynamic nature.
The proposed Minimum Viable Evaluation Suite (MVES) provides a structured, audit-oriented approach to LLM application evaluation, moving beyond conventional software testing paradigms.
- · LLM application developers prioritizing reliability
- · Organizations implementing LLM-powered solutions
- · AI safety and ethics researchers
- · Consulting firms specializing in AI validation
- · Developers neglecting robust evaluation
- · Organizations deploying unchecked LLM applications
- · Traditional software testing methodologies for AI
More reliable and trustworthy LLM applications will emerge due to improved evaluation practices.
Standardization efforts for LLM evaluation will accelerate, leading to industry benchmarks and certifications.
The adoption of complex agentic AI systems will be de-risked, expanding their integration into critical workflows.
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Read at arXiv cs.CL