Turn a constitution of rules into a model that follows them, with no human labels on harmful examples. The strong answer walks the two phases (self-critique SFT, then RL from AI feedback), then spends real time on the part papers gloss: adversarially validating the aligned model holds under attack.
Updated Sep 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
Turn a constitution of rules into a model that follows them, with no human labels on harmful examples. The strong answer walks the two phases (self-critique SFT, then RL from AI feedback), then spends real time on the part papers gloss: adversarially validating the aligned model holds under attack.
The screen is whether the candidate knows the actual CAI mechanism (self-critique and revision for SFT, then a preference model trained on AI-labeled comparisons for the RL phase) and does not confuse it with vanilla RLHF that needs human harm labels. The production senior move is the validation phase: a held-out adversarial suite, per-rule pass rates, and a regression gate, because a model that follows the constitution on the train distribution but folds under a jailbreak is not production-safe. Watch for the candidate who describes RLHF and calls it Constitutional AI.
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