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March 31, 2026
In the world of instructional design, we often talk about the “scope-to-time ratio.” It is the delicate balance between the depth of the material and the amount of time a learner has to absorb it. When our team was brought together to develop an introductory AI Prompting Course for Bisk, we were handed a challenge that tested the very limits of that balance.
The project parameters were clear but daunting: we were tasked with creating a module for absolute beginners—specifically university faculty and corporate professionals—that had to be completed in a window of 10 to 30 minutes.
The complexity of the project didn’t stem from the technology itself, but from the requirements. The client provided a specific list of five mandatory learning objectives that had to be covered in full:
When our team first analyzed these requirements against the 10-to-30-minute timeframe, our initial reaction was one of caution. From an instructional standpoint, covering five distinct pillars of a brand-new technology in such a short window felt like an impossible task if we wanted to avoid cognitive overload. Our team’s first instinct was to recommend narrowing the scope—focusing on fewer objectives to allow for the deep, iterative practice that builds true mastery.
However, the client was firm. These five objectives were “points of interest” mandated by internal stakeholders. They didn’t just want a course on how to write a prompt; they wanted a comprehensive, yet brief, organizational on-ramp.
Faced with this constraint, our team had to fundamentally rethink our approach. We realized that if we couldn’t go deep into the technical weeds of every objective, we had to make the “surface level” incredibly high-impact. We shifted our strategy from creating “prompt engineers” to building “digital comfort.”
To make the full 30 minutes of the course as efficient as possible, we focused on surgical simplicity. One of our most significant decisions was how to define the core concept of the course. Rather than spending time on the architectural differences between Large Language Models, we framed the prompt through a purely user-centric lens. We defined a prompt simply as: “The instruction, question, or request you give an AI system to generate a response.”
By using language that was immediately accessible, we were able to satisfy the “Definition” objective in seconds, leaving more of our limited “seat-time” for the hands-on practice scenarios in Education, Business, and Administration.
By the time we reached the final build, we had utilized every second of the 30-minute maximum. What initially felt like an unrealistic constraint ended up being the project’s greatest strength. By being forced to strip away the “nice-to-know” technical jargon, we created a module that was lean, professional, and exactly what the client’s workforce needed: a sturdy, 30-minute foundation that removed the intimidation factor of AI.
This project taught our team a vital lesson in stakeholder alignment. Sometimes, the most professional response to a difficult constraint isn’t to fight it, but to refine the most basic elements of a subject until they are so clear and actionable that they provide the thorough experience the client is looking for, even in a fraction of the time.
In the next post, I’ll share the “Art of the Cut”—how our team decided which high-quality ideas (including a project mascot) had to be removed to protect the focus and professional tone of this specific course.