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Certification: Generative AI Foundations for Non-Technical Professionals

Show the world you have AI skills—gain a clear understanding of generative AI, its practical uses, and ethical considerations. This certification helps you confidently discuss and apply AI concepts in any professional environment.

Exam of 10 to 20 questionsCertificate for LinkedInBeginner
IT and development
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The exam

Take the certification exam

Multiple-choice questions about the course. Pass with 70% or more and your certificate is issued at once, with a public page and an "Add to LinkedIn" button.

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What the exam covers

2 questions from each of the 11 chapters of the course

20 multiple-choice questions, drawn fresh for every attempt. Pass with 70% or more.

  1. 01AI then vs now17:46
  2. 02Why GenAI is possible now5:00
  3. 03The less-discussed side of GenAI15:47
  4. 04Decentralised AI16:17
  5. 05LLM APIs6:58
  6. 06LLM app framework and text completion3:02
  7. 07Chatbots4:17
  8. 08RAG: LLM with knowledge10:29
  9. 09LLMs for downstream NLP3:14
  10. 10Agents based on LLMs9:15
  11. 11LLM OS3:28
About the course

The Certification: Generative AI Foundations for Non-Technical Professionals provides a comprehensive introduction to generative AI concepts and their real-world applications. Participants will gain valuable skills such as improved decision-making, increased productivity, and adaptability in a rapidly evolving workplace. Enroll today to build a strong foundation in generative AI and enhance your professional growth.

This certification covers the following topics:

  • The Evolution from Analytical to Generative AI
  • The Power of Large Models and Transformer Architecture
  • Current State of Generative AI Modalities
  • The Generative AI Landscape and Application Layers
  • The Rise of Open Models and Decentralized AI
  • Technical Underpinnings and Inference
  • Levels of LLM Applications
  • What exactly is generative AI and how does it differ from traditional AI?
  • What key technological advancements have enabled the rise of modern generative AI?
  • What are some practical applications of large language models (LLMs)?
  • What are the current strengths and limitations of generative AI models across different modalities (text, code, image, video, audio)?
  • What are some of the key challenges and concerns associated with generative AI?
  • What is the significance of "decentralized AI" in the context of generative AI?
  • What is multimodality in AI, and why is it important?
  • What is the Transformer architecture, and why is it important for generative AI?
  • What is hallucination in LLMs, and why is it a challenge?
  • What is Retrieval Augmented Generation (RAG), and how does it enhance LLMs?
  • What is function calling in LLMs, and why is it significant?
  • What are AI agents, and what functionalities do they offer?