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.

Hiring signal: A candidate who can distinguish factual hallucination from contextual hallucination, and who has built a grounding verification pipeline rather than just calling an API, demonstrates the technical depth that RAI engineering roles require.

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

Why This Lesson Matters

Every LLM hallucinates. This is not a bug — it's a fundamental property of how language models work. They predict the most likely next token based on patterns in training data, not based on ground truth. When a model says "The Eiffel Tower was built in 1887," it's not retrieving a fact; it's generating a sequence of tokens that statistically resemble factual statements it saw during training.

For a RAI engineer, the question isn't "how do we stop hallucination" (you can't entirely) but "how do we detect it, measure it, and reduce it to acceptable levels for a given use case?"

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Types of Hallucination, Detection Methods, Mitigation Strategies, Measurement, Key Takeaways — plus a hands-on lab, quiz, and project artifact.

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