AI & Technical question

Design a RAG-based system to support content moderation at scale, specifically for identifying misinformation.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Understanding of how retrieval augmented generation applies to a moderation pipeline, and how to measure its accuracy on a hard, ambiguous problem.

Clarify scope: text posts, video transcripts, or both, since detection differs by modality.
Define the retrieval corpus as a maintained set of fact checked claims and trusted sources, kept current.
Design the pipeline to retrieve similar claims for a flagged post, then assess consistency and confidence rather than issuing a final verdict alone.
Route low confidence or high reach content to human reviewers instead of auto removing.
Choose metrics: precision and recall against a labeled set, plus reviewer overturn rate.
Confirm whether this targets known false claim matching or novel claim detection, since the latter is harder.

Distinguishes RAG's role, grounding the model in verified sources, from a pure classifier, and explains why grounding reduces hallucinated verdicts.
Proposes a confidence threshold with human review for high reach content, not full automation.
Names specific metrics: precision on flagged content, time to detect a new false claim, reviewer overturn rate.

Presenting RAG as a fully automated verdict system, ignoring the reputational risk of false positives.
Not addressing how the fact source corpus stays current and trustworthy.

How would you handle a claim true in one country's context but false in another?
How do you keep the retrieval corpus from becoming stale?