Design a Jira+Confluence RAG assistant

Q: Design a Jira+Confluence RAG assistant

This question evaluates competencies in designing retrieval-augmented generation systems, including ingestion and indexing, retrieval and ranking strategies, prompt-based generation and citation handling, permission-aware access control, and monitoring for freshness and latency.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Question

You are asked to design a simple Retrieval-Augmented Generation (RAG) system that answers employee questions using content from two internal products: Jira (issues, comments) and Confluence (pages, spaces).

Design an MVP that supports “cross-product” questions such as:

“What is the current status of project X and what design doc explains the latest decision?”
“Summarize the open P0 bugs for service Y and link the relevant runbook page.”

Requirements and constraints:

Data sources: Jira issues (title/description/comments/metadata) and Confluence pages (title/body/metadata).
Must respect document-level permissions (a user should only retrieve what they can access).
Latency target: interactive Q&A (a few seconds).
Results should cite sources (links to Jira issues / Confluence pages).
Handle updates: Jira changes frequently; Confluence changes less frequently.

Describe:

Ingestion and indexing (chunking, embeddings, metadata)
Retrieval strategy (filters, ranking, hybrid search)
Prompting / generation and citation handling
Permission enforcement approach
Evaluation and monitoring (offline + online)

Design a Jira+Confluence RAG assistant

Quick Overview

Solution

Comments (0)