OneRange Vero

AI Foundations: How Generative AI Works

This course gives every employee a clear mental model of how generative AI actually works, from tokens and prediction to training data and context windows. Learners explore what today's models can and can't do, why they sometimes produce confident-but-wrong answers, and how the major tools fit into the landscape. By the end they share a common vocabulary and realistic expectations for using AI at work.

What this course covers

This course gives every employee a clear mental model of how generative AI actually works, from tokens and prediction to training data and context windows. Learners explore what today's models can and can't do, why they sometimes produce confident-but-wrong answers, and how the major tools fit into the landscape. By the end they share a common vocabulary and realistic expectations for using AI at work.

The course runs across 6 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

  • Demystifying the Buzzwords: AI, Machine Learning, and Generative AI — Establish a clear mental model of the AI hierarchy. Learners evaluate business use cases to determine whether they require traditional analytical AI, machine learning forecasting, or generative content creation.
  • Under the Hood: Tokens, Probability, and Next-Token Prediction — Explore how models process text as tokens and use probability to predict the next word. Learners navigate scenarios where they must choose prompts that steer prediction pathways to avoid generic outputs.
  • The Raw Material: Training Data and How Models Learn — Examine the role of pre-training data, fine-tuning, and human feedback (RLHF). Learners act as project leads deciding how to address data bias and copyright considerations when deploying a model.
  • Managing the Sandbox: Context Windows and Retrieval-Augmented Generation (RAG) — Understand the limits of model memory and how context windows work. Learners practice selecting the best architecture (long-context vs. RAG) to solve specific information-retrieval business problems.
  • Capabilities, Limits, and the Truth About Hallucinations — Analyze why models confidently generate incorrect information. Through interactive scenarios, learners review AI-generated drafts to identify subtle logical errors, factual hallucinations, and security risks.
  • Navigating the Model Landscape: Choosing the Right Tool for the Job — Compare proprietary versus open-source models, and large frontier models versus small, specialized models. Learners evaluate realistic budget, privacy, and performance trade-offs to select the optimal model for a team project.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to Data Analysis, Generative AI Concepts, Critical Thinking, Content Strategy, AI Ethics.

  • Data Analysis
  • Generative AI Concepts
  • Critical Thinking
  • Content Strategy
  • AI Ethics

Who it is for

All employees. The material is pitched at beginner level, and takes roughly 120 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Foundation track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including branching scenario exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a quiz assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Related

Course code FND-101.