This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design enterprise RAG search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
##### Question
Design an end-to-end Retrieval-Augmented Generation (RAG) search system for enterprise users, covering architecture, data ingestion, retriever and generator selection, indexing, latency, security, and scalability.
Quick Answer: This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design enterprise RAG search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
hardMachine Learning EngineerTechnical ScreenML System Design
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Design enterprise RAG search system
Design an End-to-End Enterprise RAG Search System
Background
You are tasked with designing a Retrieval-Augmented Generation (RAG) search system for enterprise users. The system should allow employees to ask natural-language questions and receive grounded, cited answers using their organization’s private documents and tools.
Assume a multi-tenant, cloud-hosted setup with strict security and compliance requirements. Content spans PDFs, Office docs, wikis, tickets, chats, and databases. Scale assumptions (adjust as needed):
1,000+ active users; 10–100 queries/sec peak.
10–100 million text chunks indexed across tenants; up to 1 million new/updated documents per day.
Data freshness target: under 5 minutes from change to searchable.