In my opinion, the most important feature in a Retrieval-Augmented Generation (RAG) tool is retrieval accuracy, because the quality of AI-generated responses heavily depends on how effectively the system retrieves relevant, reliable, and contextually appropriate information from external knowledge sources. Accurate retrieval ensures that the AI model works with the right data, reducing hallucinations, improving factual correctness, and generating more trustworthy responses. While features like scalability, integration capabilities, latency, and response generation quality are also important, they are most effective when the retrieved knowledge is precise and relevant. Therefore, strong retrieval accuracy directly improves AI response quality, enhances knowledge discovery, and increases user trust in AI-driven systems.