AI Hallucinations Management & Fact Checking in LLMs

About this course

Hallucinations happen. Large Language Models (LLMs) like ChatGPT, Claude, and Copilot can produce answers that sound confident—even when they’re wrong. If left unchecked, these mistakes can slip into business reports, codebases, or compliance-critical workflows and cause real damage.

What this course gives youA repeatable system to spot, prevent, and fact-check hallucinations in real AI use cases. You’ll not only learn why they occur, but also how to build safeguards that keep your team, your code, and your reputation safe.

What you’ll learn

What hallucinations are and why they matter

The common ways they appear across AI tools

How to design prompts that reduce hallucinations

Fact-checking with external sources and APIsCross-validating answers with multiple models

Spotting red flags in AI explanations

Monitoring and evaluation techniques to prevent bad outputs

How we’ll work

This course is hands-on. You’ll:

• Run activities that train your eye to spot subtle errors
• Build checklists for verification
• Practice clear communication of AI’s limits to colleagues and stakeholders
• Why it matters

By the end, you’ll have a structured workflow for managing hallucinations. You’ll know:

• When to trust AI
• When to verify
• When to reject its output altogether
• No buzzwords. No hand-waving. Just concrete skills to help you adopt AI with confidence and safety.

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