Evaluating Large Language Model (LLM) applications differs from traditional software testing because outputs are stochastic, high-dimensional, and sensitive to prompt and model changes. We present an evaluation-driven workflow - Define, Test, Diagnose, Fix - that turns these challenges into a repeatable engineering loop. We introduce the Minimum Viable Evaluation Suite (MVES), a tiered set of recommended evaluation components for (i) general LLM applications, (ii) retrieval-augmented generation (RAG), and (iii) agentic tool-use workflows. We also synthesize common evaluation methods (automated checks, human rubrics, and LLM-as-judge) and discuss known judge failure modes. In reproducible local experiments (Ollama; Llama 3 8B Instruct and Qwen 2.5 7B Instruct), we observe that a generic "improved" prompt template can trade off behaviors: on our small structured suites, extraction pass rate decreased from 100% to 90% and RAG compliance from 93.3% to 80% for Llama 3 when replacing task-specific prompts with generic rules, while instruction-following improved. These findings motivate evaluation-driven prompt iteration and careful claim calibration rather than universal prompt recipes. All test suites, harnesses, and results are included for reproducibility.

翻译：评估大型语言模型（LLM）应用与传统软件测试不同，因为其输出具有随机性、高维性，并且对提示词和模型变化敏感。我们提出一种评估驱动的工作流程——定义、测试、诊断、修复——将这些挑战转化为可重复的工程循环。我们引入最小可行评估套件（MVES），这是一个分层推荐的评估组件集合，适用于（i）通用LLM应用，（ii）检索增强生成（RAG），以及（iii）智能体工具使用工作流。我们还综合了常见的评估方法（自动检查、人工量规和LLM即评判器），并讨论了已知的评判器失效模式。在可复现的本地实验中（使用Ollama平台；Llama 3 8B Instruct和Qwen 2.5 7B Instruct模型），我们观察到通用的"改进型"提示模板可能导致行为权衡：在我们的小型结构化测试套件中，当用通用规则替换任务特定提示时，Llama 3的提取通过率从100%降至90%，RAG合规性从93.3%降至80%，而指令遵循能力却有所提升。这些发现表明，需要采用评估驱动的提示词迭代和审慎的声明校准，而非依赖通用的提示词配方。所有测试套件、工具框架和结果均已包含，以确保可复现性。