The evaluation of large language models (LLMs) has traditionally relied on static benchmarks that prioritize performance metrics such as accuracy, precision, BLEU and standardized tests. However, these metrics frequently fail to capture the deeper reasoning, consistency, and real-world applicability of these models. This paper critically examines the limitations of the current benchmarks and preliminarily introduces a practical, context-sensitive framework to qualitatively evaluate LLMs. Through two case studies, which include one for medical diagnosis and the other for traffic engineering, we highlight key shortcomings in existing evaluation approaches. Despite achieving accuracy of 92% in diagnosing diseases and solving multi-step reasoning tasks in the traffic engineering case with 62% accuracy, the evaluated LLM displayed critical gaps, such as overlooking ethical considerations, practical constraints, and failing to maintain consistency across varied prompts. Expert evaluations further revealed issues in reasoning transparency and technical feasibility. This might raise questions about trust in the applicability of LLM systems. By emphasizing domain-specific tasks, expert involvement, and consistency analysis, this study advocates for a paradigm shift in how we benchmark LLMs. The findings underline the urgent need for more nuanced evaluation frameworks to ensure LLMs can reliably support complex, real-world decision-making.