Improve Trust in a RAG System
Company: Freddie Mac
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Technical Screen
You own an enterprise retrieval-augmented generation system used for high-stakes document question answering, such as mortgage underwriting, legal review, or compliance analysis. Users hesitate to trust the answers because they cannot easily verify the source evidence, understand uncertainty, or know when the system might hallucinate.
Design how you would increase user confidence in this RAG system. Cover the product experience, retrieval and generation architecture, confidence scoring, calibration, verification, fallback behavior, human feedback, monitoring, privacy, compliance, and adversarial testing.
Quick Answer: This question evaluates system-design competency for trustworthy retrieval-augmented generation, probing skills in retrieval and generation architecture, confidence scoring and calibration, evidence verification and fallback mechanisms, monitoring, privacy and compliance, human feedback integration, and adversarial robustness.