Six modules built around real work. Pre-work intake tools that shape every session to the participant. A closing that measures your growth from where you started.
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1. AI literacy check
Eight questions establishing your baseline — what you know, what concerns you, and what you most want to achieve. Shapes how your training is delivered.
Self-administeredEstablishes baseline
2. Drudge work deconstructor
Identifies your repetitive tasks, time costs, domain expert handoffs, and the work you most want to protect. Results shape the entire workshop.
Self-administeredShapes your training
What is an LLM and how does it actually work?
Plain English. What the model was trained on. Why it sounds confident even when it is wrong. Why it is a thinking tool that requires direction.
Foundational literacy
Claude vs. Gemini vs. ChatGPT — practical differences
When each tool shines. Why the choice of tool matters less than how you use it. Inspired by the comparative work of Jules White and colleagues at Vanderbilt University.
ClaudeGeminiChatGPT
Personal accounts vs. business accounts
Consumer tools vs. enterprise versions. Data handling differences. What your IT and legal teams need to answer before you use AI for work data.
Consult IT and legalNo PII in consumer tools
RAG for complete beginners
Retrieval-Augmented Generation in plain English. The filing cabinet analogy. Why an AI that can look things up in your documents changes what it can do for you.
Conceptual foundation
Hands-on with NotebookLM
Google's free RAG tool. Upload a real document from your own work, ask questions, get grounded answers. Ideal for Google Workspace users.
NotebookLMGoogle WorkspaceImmediate practical skill
When RAG helps and when it does not
RAG is powerful for your own documents but has limits. Why you still verify the output. The domain expert still reviews the conclusion.
Verify outputs alwaysCritical thinking habit
Projects, Gems, and custom GPTs — same idea, three platforms
Claude Projects, Gemini Gems, and ChatGPT custom GPTs all do the same fundamental thing: give an AI a standing context, a persona, and a purpose.
Claude ProjectsGemini GemsCustom GPTs
Build one live — hands-on exercise
Each participant builds an augmentor around one real task from their drudge work results. They test and refine it before the session ends.
Leave with a working toolUses pre-work results
Adding a style guide
How to give your augmentor your organization's voice, tone, and formatting standards. Every output sounds like your team, not like a generic machine.
Brand consistency at scale
Pattern 1 — Persona
Tell the AI who it is being, with specificity. "Act as a small business accountant with 20 years of sole proprietor experience" is strong. "Act as an expert" is not.
Persona patternBeginner entry point
Pattern 2 — Template
Give the AI an exact output structure before it answers. Removes unpredictability — the most common frustration for new users.
Template patternImmediate workplace value
Pattern 3 — Flipped Interaction
Tell the AI your goal and ask it to interview you until it has everything it needs — one question at a time. The AI becomes the expert gathering requirements. This is the confidence breakthrough moment.
Flipped interactionThe confidence breakthrough
Using all three together
Persona establishes the expert. Flipped Interaction gathers what it needs, one question at a time. Template delivers the output in exactly the right shape.
Combined workflowIntermediate mastery
How to push back on bad output
What to do when the AI gets it wrong. How to recognize a hallucination. When to stop entirely and pick up the phone.
Verify everythingCritical output review
What AI trains on and what that means for your data
How consumer AI tools handle the text you type. What PII, client names, and proprietary data mean in this context. This module raises the right questions — your IT and legal teams answer them.
Consult IT and legalNo PII in consumer toolsAwareness foundation
The domain expert principle
AI prepares you for the expert. It does not replace the expert. Attorneys, accountants, IT security professionals — they hold accountable knowledge AI cannot certify.
Core philosophy
The literacy check — revisited
Participants retake the opening quiz. Not a test. A mirror. The goal is for them to see their own growth from the beginning to the end.
Visible confidence growth
One thing I will stop doing manually
Each participant commits to one drudge task they will hand to their augmentor this week. Followed up in a 30-day check-in.
Accountability commitment
How to keep growing
How to stay current as tools evolve. For those who want to go deeper — Professor Jules White's prompt engineering course on Coursera is the recommended next step.
Ongoing learning pathCoursera