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2026 Fixing AI Errors & Hallucinations, And Fact-Checking

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2026 Fixing AI Errors & Hallucinations, And Fact-Checking

This course is part of AI For Business Productivity - 2026 Specialization

Instructor: Alex Genadinik

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

1 hour to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

1 hour to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the AI For Business Productivity - 2026 Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

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 How to spot faulty logic, outdated knowledge, and reproducible mistakes.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.