Deepchecks offers a Root Cause Analysis (RCA) approach to improve the evaluation and performance of Retrieval-Augmented Generation (RAG) systems in Large Language Models (LLMs), which are prone to hallucinations. Traditional evaluation protocols often fail to identify the root causes of errors, such as retrieval issues or prompt weaknesses, which can lead to unsupported, risky answers. Using Deepchecks RCA, users can identify specific failure points in a RAG pipeline, such as poor retrieval ranking and ungrounded advice-style responses. By applying targeted fixes like updating prompts to ensure reliance on provided context and adding a reranking model to improve data retrieval ranking, users have seen significant improvements in the model's accuracy and its ability to produce grounded answers. Deepchecks RCA not only enhances offline evaluations but can also be integrated into production systems for continuous monitoring and improvement, offering a systematic way to address and resolve model failures across various LLM applications.