The comprehensive guide outlines the evolution and development of evaluation frameworks for Large Language Models (LLMs) up to 2025, emphasizing the need for these frameworks to extend beyond traditional offline benchmarks to include production monitoring, safety, and context-awareness. It highlights the importance of combining LLM evaluations using LLM-as-a-Judge with human reviews for scalable and trusted evaluation pipelines, facilitated by platforms like Deepchecks which offer real-time monitoring, trace tagging, and CI/CD support. The guide discusses various evaluation metrics, including accuracy, fluency, and robustness, and introduces new methods such as contextual faithfulness and dynamic domain boundary monitoring informed by regulatory requirements like the EU AI Act. It underscores the significance of designing specific evaluation scenarios, such as standard, edge, and adversarial cases, and details the ethical considerations and challenges in LLM evaluation, advocating for a collaborative approach among researchers, developers, and ethicists to ensure the ethical and effective deployment of LLMs.