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

Hallucination Detection & Mitigation

A hallucinating model isn't broken. It's doing exactly what it was trained to do — predict the next token. The responsibility for grounding is ours.

What you will learn

Classify hallucination types: factual, contextual, and fabricated citations
Build a grounding verification pipeline that checks LLM claims against source documents
Implement factuality scoring using NLI models and LLM-as-judge
Apply self-consistency checking and chain-of-verification to reduce hallucination
Design mitigation strategies: RAG grounding, confidence calibration, and abstention

Introduction

Hallucination Detection & Mitigation

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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