Design a RAG-based assistant service
Company: Microsoft
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Onsite
## Scenario
You need to build a Retrieval-Augmented Generation (RAG) assistant for an enterprise product. It should answer questions using internal documents and return grounded answers with citations.
## Task
Design the end-to-end RAG system.
### Requirements
- Multi-tenant (each enterprise/customer isolated).
- Access control enforcement (document-level and snippet-level).
- Freshness: new/updated docs searchable quickly.
- Citations and low hallucination rate.
- Observability and evaluation.
### What to cover
- Ingestion and indexing pipeline (chunking, embeddings).
- Retrieval, reranking, and context assembly.
- Generation strategy and citation mechanism.
- Safety/PII handling.
- Metrics and testing strategy.
Quick Answer: This question evaluates system-design and machine-learning engineering competencies related to Retrieval-Augmented Generation, including architecture for retrieval and indexing, access control and tenant isolation, freshness and observability, citation and hallucination mitigation, and safety/PII handling.