human in loop

An agent that performs actions is far riskier than one that only talks. The signal is action-level guardrails: least privilege, argument validation, human approval for irreversible actions, and a blast-radius mindset. Here is the answer.

Moderation is a multi-stage classification problem with harsh tradeoffs: false negatives cause real harm, false positives silence legitimate users, and the adversary keeps adapting. What interviewers reward: the tiered pipeline, per-severity precision/recall calibration, and human-in-the-loop. This lays out the design.

Screening resumes is both high-volume and high-stakes: a legally sensitive, bias-prone judgment about people. The signal is trading off throughput against fairness, human oversight, and explainability, not an LLM ranking resumes. Here is the design.

Healthcare is the highest-stakes AI domain: a wrong answer can injure a patient, and regulators are paying attention. The signal is designing decision support with grounding, human oversight, and guardrails, never an autonomous diagnoser. Here is the design.

Legal review is high-stakes and precision-critical: a missed clause or hallucinated citation carries real consequences, and lawyers have been sanctioned over fabricated cites. The signal is grounding in the actual documents, citing exact passages, and lawyer-in-the-loop. Here is the design.

The root cause is not a weak system prompt. A model trained to be agreeable is dangerous to a user in crisis, because the objective and the safety requirement point in different directions. The fix has to live outside the model. Here is what that architecture looks like.

Trust is an interface and reliability problem, not a messaging one, and the goal is not maximum trust. Here is the mechanism list that actually moves it, the recovery path after a public failure, and the metric most teams optimize in the wrong direction.