Microsoft·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

May 2026

Summary

Microsoft ML Engineer system design round, one big open-ended question about building a RAG QA system for an enterprise knowledge base. The scope was massive and I kept second-guessing whether to go deeper on retrieval or spend more time on the agentic bits. Left feeling like I covered the surface of everything and the depth of nothing.

Questions Asked (1)

Design a RAG-based question-answering system for an enterprise knowledge base, where documents change over time, questions can be multi-step, and the system may call external tools like search or a calculator.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

This one sprawled in every direction and I never fully got my footing.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then outline a high-level architecture covering ingestion, retrieval, generation, and tool use. Dive into key components like incremental indexing, multi-hop reasoning, and tool integration, discussing trade-offs and evaluation metrics.

Pro tip: Emphasize the importance of a feedback loop for continuous improvement, including user feedback and automated evaluation, to handle evolving documents and queries. Also, mention the need for caching and rate limiting when calling external tools to manage cost and latency.

1. Clarify Requirements and Constraints

Ask about scale, document types, update frequency, latency, accuracy, and budget. Understand what external tools are needed and how multi-step questions are defined.

2. High-Level Architecture

Outline components: document ingestion and indexing, retrieval, generation, and tool orchestration. Explain how they interact and data flow.

3. Handling Dynamic Documents

Describe incremental indexing strategies, versioning, and change detection. Discuss how to keep the index fresh without full reindexing.

4. Multi-Step Reasoning and Tool Use

Explain how to decompose complex questions, chain retrieval and generation steps, and integrate external tools via APIs. Mention planning and execution frameworks.

5. Evaluation and Monitoring

Propose metrics for retrieval and generation quality, and for tool use. Discuss A/B testing, user feedback, and continuous improvement.

Key Points to Mention

Incremental indexing and change data capture for dynamic documents
Hybrid retrieval (dense + sparse) and re-ranking for better accuracy
Query decomposition and multi-hop reasoning techniques
Tool integration via function calling or APIs, with error handling and caching
Evaluation metrics: retrieval recall, answer correctness, tool success rate
Scalability and cost considerations: caching, batching, and rate limiting

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.