Core

Constitutional AI and RLAIF

RLAIF (RL from AI Feedback) replaces human preference labels with AI-generated ones, scaling alignment past the human-labeling bottleneck. Constitutional AI is Anthropic's specific approach: the model critiques and revises its own outputs against a written set of principles (a constitution), generating the preference data from those principles. The win is scalability, consistency, and explicit, editable values; the risk is the AI judge's own biases. Applied-AI interviews probe it because it is how alignment scales and how values become explicit and auditable.

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COURSES COVERING THIS TOPIC

No lesson covers this one directly yet. These teach the surrounding topic from the beginning.

Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.

RELATED CONCEPTS

RLHF: Reinforcement Learning from Human Feedback Reward Models DPO and Preference-Optimization Variants Jailbreaks and Red-Teaming Taxonomy

PRACTICE THIS IN REAL QUESTIONS

LLM & GenAI FundamentalsWhat is Constitutional AI / RLAIF, and how does it differ from RLHF?→LLM & GenAI FundamentalsWalk through RLHF, then explain DPO and why it has largely displaced PPO-based RLHF.→LLM & GenAI FundamentalsExplain PPO and GRPO for LLM alignment. Why did GRPO drop the value model?→LLM & GenAI FundamentalsAfter RLHF, your model is safer but worse at hard tasks. How do you manage the alignment tax?→LLM & GenAI FundamentalsYour RLHF model games the reward model instead of being genuinely helpful. How do you stop reward hacking?→LLM & GenAI FundamentalsWhat is reward-model overoptimization, and how do you detect and bound it during RLHF?→

COMPANIES THAT ASSUME THIS

OpenAI Anthropic Google Microsoft Scale AI Decagon

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