Not all AI mistakes are the same. Knowing the difference can save you time, money, and headaches. This course gives you the skills to identify, debug, and prevent AI hallucinations and errors across different use cases, from natural language generation to coding assistants.

We start with the fundamentals: What is an AI hallucination? How to detect fabricated facts, fake citations, and confident falsehoods. What is an AI error? How to spot faulty logic, outdated knowledge, and reproducible mistakes. Quick reality-check techniques to verify AI output before it causes harm. Best prompting strategies to reduce risk and improve accuracy. Then we move into AI code assistant errors: Debugging incorrect AI-generated code. Avoiding subtle logic bugs and broken dependencies. Testing AI-written functions before deployment. Combining human review with AI-generated solutions for reliable output. We’ll also cover real-world case studies where misunderstanding an AI’s mistake led to costly outcomes, and how small changes in workflow could have prevented them. You’ll see how these lessons apply not only to text and coding assistants, but also to AI-driven data analysis, customer service bots, and decision support systems. Finally, you’ll learn a systematic AI output verification framework you can apply to any LLM, whether it’s ChatGPT, Claude, Gemini, or open-source models. This framework ensures you catch misinformation, prevent damaging decisions, and maintain quality in both everyday AI tasks and high-stakes professional work. By the end of this course, you’ll be able to: Tell hallucinations and errors apart instantly. Design prompts that minimize AI mistakes. Verify facts and sources efficiently. Debug AI code assistant output with confidence. Perfect for developers, tech professionals, and anyone using AI tools for content, decision-making, or coding.