A Few Important Notes on These Materials

As you explore these documents and the concepts within them, please keep two important things in mind:

1. This Course Focuses on LLMs, But AI is a Much Broader Field

The vast majority of the tools, examples, and discussions in this collection are centered on Large Language Models (LLMs) and Generative AI. This is intentional, as these are the technologies driving the current, most accessible wave of business transformation.

However, the field of Artificial Intelligence is much wider. It also includes:

• Classic Machine Learning: For predictive modeling with structured data (e.g., sales forecasting, churn prediction).
• Computer Vision: For analyzing images and video (e.g., quality control on a production line).
• Robotics and Reinforcement Learning: For autonomous physical systems.

While we touch on some of these, our primary focus is on the tools you and your teams can use today to augment your thinking and workflows.

2. Capabilities Differ Between Models (and Evolve Constantly)

You will see examples of capabilities that may work better—or work at all—only in specific AI models (like OpenAI's ChatGPT-4o versus Google's Gemini, for instance). An AI's ability to analyze a spreadsheet, create a chart, or interpret a complex image is not yet a universally standardized feature.

Why this matters for you:

• Some examples shown here are designed to broaden your knowledge of what is possible with the best available tools, even if those specific features haven't been enabled in your secure, enterprise version of Gemini yet.
• The AI landscape changes weekly. A feature that one model has today is very likely to be a standard feature in all major models in the near future.
• Therefore, think of these examples as a preview of the capabilities that will soon be at your fingertips. The goal is to build your intuition for what's coming, so you are ready to leverage new features the moment they become available in your approved environment.

3. Always Apply Common Sense, Security, and Your Organization's Policies

The ideas in this course are designed to inspire you to experiment and innovate. However, this creativity must always be grounded in responsible practices.

Before you apply any new technique or use a tool with your own work:

• Think About Security & Privacy: Always consider the sensitivity of the data you are using. Are you working with confidential client information, internal strategy documents, or personal employee data?
• Adhere to Your Policies: Your organization has specific policies regarding data security, privacy, and the use of third-party tools. These policies are your primary guide. The principles in this course are meant to supplement, not replace, them.
• Use Common Sense: If something feels like it might be a risk, it probably is. When in doubt, always consult with your IT, security, or legal teams before proceeding. They are your partners in innovating safely.