LangChain and Ragas collaboratively address the need for new evaluation metrics in developing reliable QA systems, moving beyond traditional ML ops metrics. Ragas is a framework that evaluates QA pipelines by focusing on two key components: retrieval and generation, using metrics such as context relevancy, context recall, faithfulness, and answer relevancy. These metrics leverage Large Language Models (LLMs) to provide insights into the system's performance without requiring extensive labeled data. LangSmith complements Ragas by offering a platform for continuous evaluation, visualization, and dataset management, thus enhancing the robustness and real-world applicability of QA systems. Together, Ragas and LangSmith facilitate a comprehensive evaluation process, allowing teams to develop and refine LLM applications efficiently.