Topic overview
Study input guardrails (prompt injection and jailbreak detection, PII redaction), output guardrails (toxicity/content filtering, structured-output validation), bias and fairness evaluation, red-teaming and adversarial testing, and human-in-the-loop escalation for high-stakes decisions.
Core concepts
Rule-based filters vs. classifier-based vs. LLM-as-judge guardrails, the strictness-vs-false-positive tradeoff, bias/fairness metrics across user subgroups, red-teaming as adversarial evaluation before launch, audit logging for post-hoc review, and defense-in-depth (no single guardrail layer is assumed sufficient alone).
Why it matters
Production AI systems fail publicly not just by being wrong, but by being unsafe, biased, or manipulated into behavior the builder never intended. Guardrails are the last line of defense between raw model output and a real user, and increasingly a regulatory requirement rather than a nice-to-have.
Interview relevance
Expect questions on how you'd design layered input/output guardrails for a chatbot, how to detect and defend against prompt injection, how to measure bias or fairness in a model's outputs, and when a system should escalate to a human instead of responding automatically.