Why One RAG Wasn't Enough: Building a Multi-RAG Pipeline for Jira Backlog Analysis

Large language models are only as good as the context you give them. While building an LLM-powered Jira Backlog Analyzer, I learned that simply adding Retrieval-Augmented Generation (RAG) wasn't enough. The real breakthrough came from treating different types of project knowledge as different kinds of organizational memory.

When Good Recommendations Aren't Useful

One of the goals of my Jira Backlog Analyzer was pretty simple: help project managers make sense of hundreds of backlog items. The analyzer groups related Jira tickets, flags potential duplicates, and generates executive summaries covering technical trends and priorities.

The first prototype worked better than I expected. Given nothing but the raw Jira tickets, the LLM could summarize clusters, spot duplicate issues, and even suggest what to do next.

Except a lot of those suggestions were generic to the point of being useless.

Why One RAG Wasn't Enough: Building a Multi-RAG Pipeline for Jira Backlog Analysis

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