Learn Prompt Engineering for free with OpenAI and Microsoft


OpenAI and Microsoft are offering a free course and tutorial on prompt engineering.

Together with investor Andrew Ng’s website DeepLearning.ai, OpenAI is offering a temporary free course on prompt engineering for developers. The course will be taught by OpenAI engineer Isa Fulford and Andrew Ng.

According to the site, the course is suitable for beginners, with the only requirement being a basic knowledge of Python. However, it is also intended to provide advanced machine learning engineers with new information on prompt engineering and how to use large language models.

Topics covered include summarization, classification, topic extraction, text transformation such as translation and proofreading, and text augmentation such as automatic email generation. The course also provides information on how to develop your own chatbot. More information and enrollment details are available here; completion time is approximately one hour.


In addition, Microsoft has created a landing page about prompt engineering techniques with numerous examples and tutorials. Among other things, Microsoft discusses the GPT-4 specific “System Messages” and the so-called “Few-Shot-Learning” using examples in the prompt.

Prompt Engineering: An emerging profession?

Prompt Engineering, ie the phrasing of instructions to a large AI model, is either a profession with a promising future or a temporary phenomenon of the current AI boom, mainly due to the shortcomings of current systems, depending on one’s perspective.

In favor of the latter thesis is the fact that language models are intended to simplify the way we operate computers. If complicated commands are necessary to achieve good results, natural language would become a kind of new programming language, which at least partially contradicts the goal of the systems.

At least for now, it appears that knowing prompt variations can give you an upper hand in quality. In addition, an important part of prompt engineering is developing ideas about what tasks large AI models can be used for.

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