What you'll learn:
- Identify and explain different types of AI hallucinations and why they occur
- Design prompts that reduce hallucinations and improve AI response accuracy
- Use RAG systems and verification techniques to fact-check AI output
- Apply monitoring and guardrails to make AI systems safer and more reliable
- Build practical workflows for detecting, preventing, and verifying AI hallucinations
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 you
A 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 APIs
Cross-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.
