Phase 2b: LLM-Specific Responsible AI · 50 min · Python · transformers · datasets

RLHF & Alignment Evaluation

Alignment is not a checkbox. It's a measurable property of a model — and if you can't measure it, you can't claim it.

Hiring signal: A candidate who can articulate specific RLHF failure modes (sycophancy, over-refusal, reward hacking) and has built evaluation tooling to measure them demonstrates frontier-level RAI engineering capability.

What you will learn

Explain what RLHF does and identify its failure modes: sycophancy, over-refusal, reward hacking
Evaluate reward model quality: accuracy, calibration, and demographic bias
Assess preference data quality: inter-annotator agreement and annotation bias
Use alignment benchmarks: HH-RLHF, MT-Bench, AlpacaEval
Evaluate Constitutional AI self-critique quality

Introduction

RLHF & Alignment Evaluation

Why This Lesson Matters

RLHF (Reinforcement Learning from Human Feedback) is the dominant method for aligning LLMs to human preferences. But alignment is not a binary state — it's a measurable property with specific failure modes. A RAI engineer must be able to evaluate whether alignment actually works, not just trust that it does.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers What RLHF Does, Where RLHF Fails, Evaluating Reward Model Quality, Preference Data Quality, Alignment Benchmarks, Constitutional AI, Key Takeaways — plus a hands-on lab, quiz, and project artifact.

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