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 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 learnWhat hallucinations are and why they matterThe common ways they appear across AI toolsHow to design prompts that reduce hallucinationsFact-checking with external sources and APIsCross-validating answers with multiple modelsSpotting red flags in AI explanationsMonitoring and evaluation techniques to prevent bad outputsHow we’ll workThis course is hands-on. You’ll:Run activities that train your eye to spot subtle errorsBuild checklists for verificationAudit AI-generated fixes in codePractice clear communication of AI’s limits to colleagues and stakeholdersWhy it mattersBy the end, you’ll have a structured workflow for managing hallucinations. You’ll know:When to trust AIWhen to verifyWhen to reject its output altogetherNo buzzwords. No hand-waving. Just concrete skills to 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.

https://www.udemy.com/course/ai-hallucinations-management-fact-checking-in-llms/