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.

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

Objective

Evaluate an LLM as a sociotechnical system across factuality, toxicity, alignment, representation, and adversarial use.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, Build, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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