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.

What difficulty level is this interview question?

This is a medium difficulty ML System Design question, commonly asked during Technical Screen rounds at Microsoft.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Microsoft during technical interviews.

Design a RAG system with agentic tools

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a candidate's ability to design a Retrieval-Augmented Generation (RAG) system with agentic tool-calling for enterprise knowledge bases, testing competencies in scalable ML system architecture, data ingestion and indexing, retrieval and reranking, prompting, tool integration, evaluation, monitoring, and safety (ML System Design domain). It is commonly asked to assess architectural reasoning, trade-off analysis, and handling of evolving document stores for grounded QA, combining practical application (system design and operational considerations) with conceptual understanding of retrieval, generation, and guardrails.

Microsoft

Feb 9, 2026, 12:00 AM

Machine Learning Engineer

Technical Screen

ML System Design

Design a Retrieval-Augmented Generation (RAG) question-answering system for an enterprise knowledge base.

Requirements:

Users ask natural-language questions; the system answers with grounded responses and citations.
The knowledge base includes documents that change over time (updates, deletions).
The system should handle multi-step questions, and may use agentic tool-calling (e.g., search, calculator, database lookup).
Discuss architecture, data ingestion/indexing, retrieval and reranking, prompting, tool use, evaluation, monitoring, and safety/guardrails.

Solution

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