The use of Retrieval-Augmented Generation (RAG) in Large Language Models (LLMs) enhances the accuracy and relevance of responses by retrieving information from external datasets, such as websites, financial databases, or company policy guidelines. However, integrating RAG into LLM applications introduces complexity in managing latency, ensuring the relevance of retrieved data, and maintaining model accuracy. To mitigate these challenges, developers can take steps to reduce latency, implement hybrid search to limit irrelevant responses, use vector databases to exclude outdated information, and scan prompts and responses to prevent accidental exposure of sensitive data. By implementing effective metadata filtering and regular updates, RAG systems can deliver contextually relevant information at scale while maintaining trust with users. Additionally, developers can utilize tools like Datadog LLM Observability to identify and troubleshoot issues in their RAG-based LLMs.