The article discusses the limitations of using a single Retrieval-Augmented Generation (RAG) system for analyzing Jira backlogs with large language models. The author explains how relying on one RAG pipeline led to incomplete or inaccurate results due to insufficient context retrieval. To address this, they built a multi-RAG pipeline that combines multiple retrieval strategies to improve the accuracy and comprehensiveness of backlog analysis. The approach demonstrates how layered retrieval can enhance LLM performance in complex, context-heavy tasks like software project management.