Quick verdict
Alison currently lists this as a beginner-level course with an average duration of about 4-5 hours. The published outcomes focus on recognizing different types of generative AI models, understanding applications, applying generative techniques to real-world problems and thinking about how new data can be generated. That makes it a reasonable orientation course rather than a complete engineering path.
What the course appears to emphasize
The value is breadth. Instead of beginning with one vendor or one chatbot, the course frames generative AI as a class of techniques and model types. That is useful for learners who need to understand what the field includes before they start chasing tools.
Who will get the most from it
A non-specialist professional can use a short course like this to learn the language of generative AI before discussing it with technical teams. A student or career-switcher can use it to decide whether the subject is interesting enough to justify a longer path. It can also help someone who has only used chat interfaces understand that generative AI extends beyond prompt writing.
What it will not give you
A short beginner course cannot substitute for building systems. If your goal is to design retrieval pipelines, evaluate LLM output, fine-tune models, manage inference costs or ship production agents, you will need coding practice and current technical documentation after this course.
How to use it well
Finish the course with a small practical task: pick one workflow you actually care about, identify where generation could help, define what a bad output would look like, and test a current model against that task. That bridge from concept to evaluation will teach you more than simply collecting course completion.
Bottom line
Choose this course when you need a compact map of the generative AI landscape. Skip it as your only learning resource if you already know the concepts and need implementation depth.