AI Prompting Foundations: Engineering Minimal Viable Competency (MVC)

Status: Final Capstone / Active Upskilling Asset.
Metric: https://seraphits.github.io/AIPromptingCourse-full/
Link: https://seraphits.github.io/AIPromptingCourse-full/

Executive Summary: Overcoming the Competency Gap

Developed as a Master’s Capstone for Bisk/USF, this project is a strategic response to the primary barrier of digital transformation: AI Anxiety. As a Learning Engineer, I architected this curriculum to move professionals past the “Initial Threshold”—transforming Generative AI from a novelty into a high-leverage functional tool for day-to-day operations.

The Challenge: Breaking the Initial Threshold

Generative AI represents a significant “threshold concept.” For many knowledge workers, the transition to using AI as a partner is blocked by a lack of structured guidance. This project provides a scalable “on-ramp,” equipping users with the Atomic Prompting frameworks necessary to achieve production-grade results immediately.

Design Under Constraint: The 30-Minute Reality

In enterprise environments, learning must fit into the flow of work. I was tasked with a strict completion constraint of 10 to 30 minutes.

The Strategy: I implemented a Minimal Viable Competency (MVC) model. Instead of explaining the underlying transformer architecture, the course focuses on the baseline frameworks required to begin prompting with high fidelity.
The Methodology: This project utilizes Atomic Instructional Design—breaking complex prompting logic into modular “atoms” of knowledge that can be immediately applied to diverse professional domains (Education, Business, and Administration).

Strategic Insight: The Ethics “Sleeper Hit”

User testing revealed a critical gap in existing AI training: users are hungry for Governance.

The Discovery: Originally a stakeholder requirement, the Ethics of AI module—covering bias identification and the limitations of LLMs—emerged as the most engaging section.
The Pivot: This feedback validated the “High-Integrity” approach of my practice. Professionals don’t just want to know how to use the tool; they want to understand the Responsibility and Oversight required to use it safely in a corporate environment.

Technical Architecture: Modular Scaffolding

The course is engineered to guide learners through the cognitive stages of AI adoption:

The Ethics Threshold: A 5-point verification framework for auditing AI-generated outputs.

Foundations: Distinguishing between effective prompting and “vibe-driven” instructions.

Agentic Practice: Hands-on scenarios designed to simulate real-world human-agent collaboration.

The Adaptive Edge – Thriving in Chaos (MC 4)

In the previous chapters of the ReadBoot Educational Operating System (RBEOS), we climbed the Maintenance Capacities (MCs) from foundational initiation to workflow mastery. But for many, the journey ends at MC 3—Routine Expertise. While routine expertise provides the “Safe Harbor” of speed, accuracy, and automaticity under stable conditions, it carries a terminal risk known as… Continue reading The Adaptive Edge – Thriving in Chaos (MC 4)

One Syllable Away from Perfection: The Category-of-Itself Paradox.

The Genesis: The Card Sort It started with a question that felt almost too big to answer: “Why do we learn?” As a UX architect, I didn’t want a poetic answer; I wanted a structural one. I gathered every piece of knowledge I could think of—from quantum physics to how to change a tire—and I… Continue reading One Syllable Away from Perfection: The Category-of-Itself Paradox.