As Large Language Models (LLMs) increasingly support and automate business-critical workflows, the need for robust evaluation frameworks becomes paramount. This paper proposes a system-level testing approach designed to assess the performance and reliability of LLM-based applications integrated into enterprise processes. Moving beyond model-centric benchmarks, the framework adopts principles from software engineering, including black-box and end-to-end testing, to evaluate real-world outcomes in retrieval-augmented generation (RAG) systems. It features modular performance indicators such as context precision, hallucination detection, business tonality alignment, and answer correctness, many harnessing the LLM-as-a-Judge methodology. Answer correctness is used as a case study for the design of interpretable performance indicators grounded in concepts from information retrieval while considering the objectives of business and technical stakeholders. Empirical evaluations in four use cases demonstrate how this approach enables organisations to validate not only the accuracy of their systems but also business relevance.