AI Hallucinations Management & Fact Checking in LLMs
AI Hallucinations Management & Fact Checking in LLMs
Published 10/2025
Duration: 2h 54m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 937.60 MB
Genre: eLearning | Language: English
Published 10/2025
Duration: 2h 54m | .MP4 1280x720 30 fps(r) | AAC, 44100 Hz, 2ch | 937.60 MB
Genre: eLearning | Language: English
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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