Introduction to Generative AI – Art of the Possible

AWS Skill Builder

FreeBeginner1 hourSelf-pacedNo coding

Last updated February 27, 2026

Introduction to Generative AI - Art of the Possible is a free, hour-long course that explains what generative AI is and what it can realistically do — no coding involved. It starts from the ground up: a quick refresher on machine learning, then the basics of how generative models work, the kinds of problems they suit, and where the genuine risks and limits sit. A running content-generation example shows the ideas in action rather than leaving them abstract, and the course closes on ethics and what adoption looks like for a small business. It is the opening course in AWS's three-part generative AI series for business and technical decision-makers, and a sensible first stop for anyone who needs a clear mental model before going deeper.

What you'll learn

  • A plain refresher on ML as the foundation GenAI builds on
  • What generative AI actually is and how it works
  • Real use cases — where it fits and where it doesn't
  • How GenAI maps onto AWS services (high level)
  • What adoption looks like, incl. for a small business
  • The ethical considerations and risks to weigh

Frequently asked questions about Introduction to Generative AI – Art of the Possible

Who is Introduction to Generative AI – Art of the Possible for?

Business and technical decision-makers, and curious beginners, who want a clear, jargon-light picture of what generative AI can and can't do — no technical background needed.

Is Introduction to Generative AI – Art of the Possible free?

Yes — Introduction to Generative AI – Art of the Possible is completely free to take.

What are the prerequisites for Introduction to Generative AI – Art of the Possible?

None.

Why we suggest this course

A fast, plain-language orientation for decision-makers and curious newcomers who want to understand generative AI's real possibilities and limits before investing time or budget. One thing to know: it is built by AWS, so the "on AWS" section and examples point to Amazon's own services (Bedrock, SageMaker, S3) — the underlying concepts are general, but expect the platform framing.

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