About This Course

Understand it. Use it. Build with it.

About the Author

Dr. Anjana Yatawara is an Assistant Professor in the Department of Mathematics . His research spans financial econometrics, statistics education, environmental justice, and applied data science.

Dr. Yatawara’s AI research includes the LLM Whisperers Study (surveying 29 AI models across five competing companies to understand what models need from users), the ELEVATE Project (three-wave assessment of AI-integrated instruction across four undergraduate courses), and a published experiment demonstrating that campus-hosted small language models with RAG can match GPT-4 for domain-specific educational tasks at 92% lower cost.

The frameworks in this course — BRIEFS and SMARTS — emerged directly from that research. They’re not teaching opinions. They’re derived from what AI models themselves, across providers, independently said they need.

Why This Course Exists

Most AI courses teach you which buttons to click. This one teaches you how to think.

The Central Valley is home to agricultural workers, healthcare professionals, teachers, county employees, small business owners, first-generation college students, and tradespeople who all face the same question: How does AI change my work, and what do I need to know?

Existing AI courses are either too technical (assume programming) or too shallow (how to write a ChatGPT prompt). This course sits in the middle: deep understanding without requiring code, practical skills grounded in research, and real examples from the fields where our students and community members actually work.

How to Use This Course

Sequential learner? Start at Module 1 and work through to Module 14. Each builds on the previous.

Already know some AI? Take the Self-Assessment and skip to the modules where your scores are lowest.

Instructor using this in a course? See the Facilitator Guide for pacing, assessment suggestions, and adaptation tips.

Just want the prompts? Go straight to the Prompt Library — 200+ ready-to-use prompts across 16 categories.

Just want the frameworks? Print the Framework Cards and pin them next to your screen.

Course Design Philosophy

This course follows five principles:

  1. Examples before theory. You see real prompts, real cases, real data before learning the framework that explains them.
  2. Central Valley relevance. Examples feature teachers, nurses, farmers, county employees, and small business owners — not just Silicon Valley engineers.
  3. Research-grounded. Every framework traces back to published research — the Whisperers study, the ELEVATE project, the SLM-RAG experiment, or cited external sources.
  4. Hands-on from day one. Every module includes guided practices and a lab. You learn by doing, not by reading about doing.
  5. Honest about limitations. AI is powerful. It also hallucinates, reinforces bias, and raises hard ethical questions. This course doesn’t shy from any of it.

Technical Details

  • Built with Quarto (open-source scientific publishing)
  • Licensed under CC BY-NC 4.0 — free to use, adapt, and share for non-commercial purposes with attribution
  • Source code available on GitHub
  • Companion platform: KernelStats — free statistical analysis

Contact

Acknowledgments

This course was developed with support from colleagues and students whose participation in the ELEVATE project and AI integration experiments generated the data that shaped these frameworks.

Back to Modules

How to Cite This Course

APA Format: > Yatawara, A. (2026). AI from the Ground Up: A Complete Practical Guide. [Online course]. https://aifromthegroundup.com

In-Text Citation: > (Yatawara, 2026)

For specific frameworks: > The BRIEFS framework (Yatawara, 2026) structures effective first messages to AI using six components: Background, Role, Intent, Execution, Format, and Stakes.

The SMARTS framework (Yatawara, 2025) guides multi-turn AI conversations through six steps: State, Map, Ask, Reason, Think, Summarize.