AI Hallucinations Management & Fact Checking in LLMs

Spot, prevent, and fact-check AI hallucinations in real workflows with AI assistants like ChatGPT

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

Requirements
- Basic knowledge of how LLMs or AI tools like ChatGPT work. Solid understanding of programming concepts and experience with Python or JavaScript. Familiarity with APIs, JSON, and basic command-line operations. Comfort with installing and running local tools or frameworks.

Description
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 tospot, prevent, and fact-check hallucinations in real AI use cases.You’ll not only learnwhythey occur, but alsohow to build safeguardsthat 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

Audit AI-generated fixes in code

Practice clear communication of AI’s limits to colleagues and stakeholders

Why it matters

By the end, you’ll have astructured 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. Justconcrete skillsto help you adopt AI with confidence and safety.

Who this course is for:
- Developers and data scientists integrating AI into production code.
- Business and compliance professionals who need reliable AI outputs.
- Teams adopting AI assistants for code, content, or decision support.
- Anyone who wants concrete methods to manage AI risk, not just theory.
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