AI for Professionals

A practical course on understanding, working with, and growing with Generative AI

3 Sessions
6 Hours of Instruction
+ Office Hours
Next Online Sessions
May 28, June 4 & 11, 2026
Thursdays at 10:00 AM Mountain Time
Register View Curriculum
Course Structure

Three Sessions to AI Proficiency

Each session includes 2 hours of instruction plus 1 hour of guided office hours

1

Understanding AI

How LLMs work and why it matters

Build a solid foundation by understanding the mechanics behind Large Language Models, their capabilities, and their limitations.

2

Working with AI

Six Habits + Professional workflows

Learn a structured approach to getting consistent, high-quality results from AI through practical habits and real-world workflow examples.

3

Growing with AI

Project review + Staying current

Share your hands-on experience with peers and develop a personal strategy for keeping pace with the rapidly evolving AI landscape.

Student Feedback

What Participants Are Saying

Hear from professionals who have completed AI for Professionals and put these skills to work.

“

This course has genuinely improved how I approach AI in my work. The engaging instruction and practical material have given me real tools to perform at a higher level. I'd strongly recommend it to any colleague considering it—it's a solid investment in professional and personal development.”

“

The information in the course was very helpful, especially how current the information was. The communication around the course was very good and helped keep me on track. Overall, very positive!”

“

Not all teachers are great teachers. Leroy is easy to listen to and makes things both simple and not overwhelming. I see this content allowing me to save time whilst still delivering high quality work. I already have several ideas where this content can and will be utilized in my day to day.”

1 Session 1

Understanding AI

Core Concepts

What is an LLM? Large Language Models trained on massive text data to predict the next token. They recognize patterns -- they don't "know" things.
How LLMs Are Built Pre-training creates raw intelligence from internet-scale data. Post-training (RLHF) shapes it into a useful assistant.
How LLMs Operate Tokenization breaks text into pieces. Attention weighs relationships between all tokens. Output is generated one token at a time.
Why This Matters Understanding the mechanics helps you prompt better, recognize limitations, and use AI more effectively.

Key Pitfalls to Avoid

Hallucinations Confident but wrong -- always verify
Context Loss Long conversations lose focus
Sycophancy Tells you what you want to hear
Garbage In / Garbage Out Vague prompts get vague results

Understanding how AI works makes you a better user. You'll prompt more effectively and know when to trust -- or verify -- the output.

2 Session 2

Working with AI

Part 1: The Six Habits

1. Define the Task Be explicit: role, goal, inputs, constraints, format
2. Provide Background Give relevant context, examples, and structured excerpts
3. Specify the Format Document type, structure, length, tone, sections
4. Request the Reasoning Ask for steps, assumptions, alternatives, and risks
5. Check the Work Cross-check facts, compare models, verify citations
6. Refine Until Right Iterate 2-3 times with specific improvement requests
Your Prompt (1-4) → AI Response → Verification (5) → Refinement (6) → Final Output

Part 2: Professional Workflows

Real-World Example: Grant Writing Apply the Six Habits across scope definition, scheduling, and budgeting with chained AI outputs
Workflow Principles Chain outputs together, verification is non-negotiable, and expect 2-3 iterations per deliverable

Take-Home Assignment

Participants apply the Six Habits to a real task from their own work and document:

  • The task and initial prompt
  • How they applied each habit
  • Verification steps and iterations
  • Final result and lessons learned

Participants present their experience in Session 3 (3-5 minutes each)

3 Session 3

Growing with AI

Part 1: Project Share-Out

Peer learning through shared experience. Participants present their take-home project (5 minutes each):

The Task What were you trying to accomplish?
The Approach How did you apply the Six Habits?
The Result What was the outcome? Demo if possible
Lessons Learned What worked? What didn't? What surprised you?

Part 2: Frontier Models & Live Demos

In this fast-moving landscape, frontier AI models evolve constantly. We review the latest releases from leading providers: Anthropic, OpenAI, Google, and xAI. We examine their capabilities, trade-offs, and real-world performance.

Then we dive into live demonstrations. You'll see how professional-grade prompts and agentic workflows in practice.

Also Covered

Trusted SourcesCurated list of reliable AI news and primary sources
Tool CategoriesOverview of AI tool landscape: assistants, copilots, specialized tools
Responsible UseTransparency, verification, privacy, bias awareness, human judgment
Building HabitsDaily, weekly, monthly practices for sustainable growth

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