Prompt Engineering
Prompt engineering is the practice of designing and refining the input — the "prompt" — you give to an AI system to get the output you want. An AI model, like a large language model or image generator, can't read your mind or do subtle interprations of language. It predicts what should come next based on the words, structure, and context you give it. Prompt engineering is learning how to shape that context deliberately.
I have a friend who teaches prompt engineering. He is in an university engineering school, but he says that in practice, it's less "engineering" in the formal engineering sense, and more applied communication. It is about figuring out how to be explicit enough that the model doesn't have to guess, without burying it in so much text that it loses the point. It is also less of a full course and more likely a component of a course.
MIT's OpenCourseWare / MIT Sloan offers short courses like "Prompt Engineering and Advanced ChatGPT" in their professional programs. Vanderbilt on Coursera is a good starting place because it's free to audit, self-paced, and built by a university. Here in New Jersey, my alma mater Rutgers, former employers NJIT and Montclair State University, and Stevens Institute list prompt engineering workshops or modules inside Computer Science, Business Analytics, and Continuing Education/workforce development programs. These might be a 1-2 day non-credit certificate, or a unit in "Applied Generative AI."
Looking at a few of these courses, you find some commonalities in what needs to be learned.
Create the prompt's instructions - what you actually want done. Vague prompts get vague results. "Write about terms of service" is vague and weak. One more specific prompt might be "define browsewrap agreements in 150 words for a non-lawyer, compare them to clickwrap, and give examples of why courts find them unenforceable." I used that prompt in writing my earlier post on terms of service.
A good prompt includes context and constraints by giving a role, audience, format, and rules to follow.
Role: "You are a tech journalist..." Audience: "...explain it to a college freshman" Format: "use a table with 3 columns" Constraints: "no legal advice, keep it under 200 words"
Some methods people use to get more reliable reasoning:
Zero-shot vs. few-shot: Show 0 examples vs. show 2-3 examples of input/output
Chain-of-thought: "Think step by step" to make it show its reasoning
System vs. user prompts: System = persistent rules, User = the specific task
Iteration: Test, see failure, add a clarification, test again
I compare much of this to when I was teaching younger K-12 students in the late 20th century about doing Internet searches. The Internet started in higher education, then took about two decades to get to K-12 in a meaningful way. In higher ed, there were significant inroads in the early 1990s with the Web and browsers, although some select schools were using it in limited ways starting in 1969. For K-12, it happened around 1996-2000, driven by the E-Rate, making the late 1990s/early 2000s when the Internet went from a lab curiosity to a daily classroom tool.
My students were using it for research papers and needed to be taught about the same kind of specificity. Assigned to write about why women were not allowed on stage in Shakespeare's time, they might type "women in Shakespeare" and get hundreds of hits about female characters in the plays. In the late 1990s, search engines were not sophisticated enough to handle a query such as "why were women not allowed to act on stage in Shakespeare's time and how did acting companies adapt to this?" Today, it can use that query/prompt. And that is something that frustrates many teachers and delights their students.


Recently, I received notice from the music service Spotify that "
The use of AI in schools is a big topic right now in the U/S. and probably in most of the developed world.