This one was harder to frame than I expected.
Start by clarifying the goal: augment human moderators, not replace them. Then walk through the RAG architecture (retrieval, generation, feedback loop) while emphasizing how each component addresses TikTok's unique challenges like scale, multilingual content, and rapid misinformation spread. Finally, discuss trade-offs and success metrics.
Pro tip: Frame the solution around reducing moderator cognitive load and time-to-action, not just accuracy—moderators are the real users, and their trust in the system is critical for adoption.
Ask about scale (daily posts), languages, latency needs, and existing moderation workflows. Confirm that the system should assist, not automate, decisions.
Outline retrieval (vector DB of verified facts, policies, past cases) and generation (LLM synthesizing evidence and suggested actions). Include a feedback loop for moderator corrections.
Discuss handling multimodal content (video, audio, text), multilingual retrieval, and real-time trending topics. Propose a hybrid retrieval approach combining semantic and keyword search.
Propose metrics like precision/recall of misinformation detection, moderator time saved, and user trust. Include A/B testing and human-in-the-loop evaluation.
Balance accuracy vs. latency, coverage vs. cost, and automation vs. human oversight. Address potential biases and adversarial attacks.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.