Design a RAG system with evaluation

Q: Design a RAG system with evaluation

This question evaluates expertise in designing Retrieval-Augmented Generation (RAG) systems, covering end-to-end architecture, document ingestion and preprocessing, embedding and indexing strategies, retrieval and reranking, prompt/context assembly, safety/fallbacks, and per-component evaluation.

Q: 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.

Question

Scenario

You are asked to design a Retrieval-Augmented Generation (RAG) system that answers user questions using a private corpus (e.g., internal docs, PDFs, knowledge base articles). The interviewer wants you to walk through each component and explain how you would evaluate each step.

Requirements

Support natural-language Q&A over private documents.
Handle frequent document updates (new/changed docs).
Provide citations or traceability to sources.
Low latency for interactive use.
Reduce hallucinations and ensure answers are grounded in retrieved context.

What to cover

End-to-end architecture and data flow.
Document ingestion and preprocessing (parsing, cleaning, chunking).
Embedding strategy and indexing (vector DB / hybrid search).
Retrieval (query understanding, top-k, filters) and optional reranking.
Prompting/context assembly and generation.
Safety/guardrails and fallback behavior when retrieval is weak.
Evaluation plan for:
- ingestion/chunking quality
- retrieval quality
- reranking quality (if used)
- generation quality and grounding
- end-to-end user success
Online monitoring and continuous improvement loop.

Design a RAG system with evaluation

Quick Overview

Scenario

Requirements

What to cover

Solution

Comments (0)