Microsoft·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jul 2026

Summary

Microsoft system design round, one big question about building a RAG pipeline end to end. Pretty dense for a single session but they clearly wanted to see if you could reason through the whole stack, not just recite buzzwords.

Questions Asked (1)

Design a Retrieval-Augmented Generation pipeline that takes a user query, retrieves relevant passages from a large corpus, ranks them, and returns the top K results to inject into an LLM prompt. Walk through ingestion, retrieval, ranking, and the trade-offs involved.

System DesignTechnical Trade-offsAPI & Integrations

Author's notes

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (corpus size, latency, accuracy) and then walk through the pipeline stages: ingestion, retrieval, ranking, and generation. For each stage, discuss design choices and trade-offs, emphasizing scalability and relevance. Conclude with evaluation metrics and potential optimizations.

Pro tip: Mention hybrid retrieval (combining dense and sparse methods) and cross-encoder re-ranking as a way to balance recall and precision, showing awareness of state-of-the-art practices.

1. Clarify Requirements and Constraints

Ask about corpus size, query volume, latency requirements, and accuracy expectations to tailor the design. This shows you consider practical constraints before diving into architecture.

2. Design Ingestion Pipeline

Explain how to process and index the corpus: chunking documents, generating embeddings, and storing in a vector database. Discuss trade-offs like chunk size and embedding model choice.

3. Implement Retrieval and Ranking

Describe the retrieval step (e.g., using approximate nearest neighbor search) and then ranking (e.g., with a cross-encoder or learning-to-rank model). Highlight the two-stage approach for efficiency.

4. Integrate with LLM and Handle Trade-offs

Explain how to inject top K passages into the LLM prompt, considering context window limits. Discuss trade-offs between K, latency, and answer quality.

5. Evaluate and Iterate

Propose evaluation metrics (e.g., recall@K, MRR, answer accuracy) and A/B testing. Mention monitoring and continuous improvement.

Key Points to Mention

Hybrid retrieval (dense + sparse) for improved recall
Two-stage ranking: bi-encoder for retrieval, cross-encoder for re-ranking
Vector database choices (e.g., FAISS, Pinecone) and indexing strategies (HNSW, IVF)
Chunking strategies and their impact on retrieval quality
Latency vs. accuracy trade-offs in retrieval and ranking
Evaluation metrics and online/offline testing

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