Build the understanding, judgment, and practical skills to work with AI.
Our AI training focuses on applied generative AI training: understanding language models, evaluating information, working with approved knowledge, connecting business tools, building agents, creating small applications, and examining AI ethics, risks, and superintelligence. We connect that understanding with practical experiments shaped around your organization.
Eight topic areas connect understanding, judgment, and hands-on building. The depth, examples, and exercises are matched to your audience and chosen format.
Understand large language models (LLMs) and how generative AI differs from traditional AI, including differences in capabilities, outputs, and practical uses.
Compare generative and traditional AI use cases, and distinguish content generation from prediction and classification.
Make sense of the different AI models available, how their capabilities differ, and how models differ from the AI-driven software and tools that use them. Explore how performance is measured through benchmarks and ranked on leaderboards, what those comparisons reveal, and where they can mislead.
Compare models and tools, interpret benchmark scores and rankings, and test whether a strong benchmark result translates into useful performance on your own tasks.
Explore prompt engineering through machine psychology: the struggle to understand LLMs’ apparent values, behaviors, biases, and how they make decisions. Approach them as if new forms of intelligence have landed on Earth—something to study, question, and understand rather than simply command. Investigate how models respond to instructions and context, and practice evaluating AI-generated information for accuracy, source quality, and uncertainty. Explore context windows—the amount of information a model can work with at once—and their limits, including token budgets for prompts and long conversations.
Design experiments that probe apparent model values and biases, compare decisions across prompts and contexts, and question what their responses do—and do not—reveal.
Explore retrieval-augmented generation to ground LLM responses in approved documents and technical reference materials, and fine-tuning to adapt model behavior through training examples. Discuss when and when not to use RAG and fine-tuning with LLMs, considering the task, available data, cost, and maintenance. Examine context windows and their limits when selecting retrieved material and deciding how to work with long documents.
Compare prompting, retrieval, and fine-tuning for a use case, examine their trade-offs, and decide whether either technique is warranted.
Connect AI to business tools through APIs (interfaces that let software systems communicate), MCPs (Model Context Protocol integrations that connect AI to tools and data), and no-code automation. Explore how these approaches differ and how information moves across a workflow.
Connect steps in a business workflow, test the information passed between tools, and identify review points.
Explore autonomous agents that use tools to pursue a task, multi-agent systems, and agent swarms. Examine agent-to-agent coordination, how agents communicate and divide work, and the risks of rogue agents that act outside intended goals or constraints.
Map an agent swarm, compare coordination approaches, and investigate how rogue-agent behavior can disrupt a shared task.
Use AI-assisted coding to turn an idea into a small application or prototype. Practice specifying requirements, inspecting generated code, testing behavior, and understanding what is needed beyond a demonstration.
Build a small application, inspect how it works, and test whether it meets the intended requirements.
Discuss ethical and compliance issues, including privacy, bias, intellectual property, and accountability, alongside AI’s environmental impact, including energy and water use. Examine risks and dangers such as cognitive atrophy—the erosion of skills through overreliance on AI—and work displacement. Explore superintelligence, recursive self-improvement, and the possibility of an intelligence explosion, distinguishing current evidence from uncertain future scenarios.
Discuss ethical dilemmas, assess risks to people and organizations, and examine assumptions about future AI capabilities and appropriate safeguards.
Topics can be packaged into programs of different lengths. Shorter formats focus on selected areas; extended programs allow more practice. Delivery, sequencing, tools, and facilitation are agreed during scoping.
Plan an AI learning seriesBegin with the decision, audience, and depth of practice you need. Formats can stand alone or connect into a broader learning plan. Session count and duration are agreed around your needs, not fixed by the topic list. Each format can be adapted to your participants and goals.
A focused discussion of AI capabilities and implications
A focused discussion of current AI capabilities, limitations, organizational implications, and opportunities worth investigating.
Shared language and clearer questions for your next AI decisions.
Guided practice with relevant tasks and examples
Guided practice with prompting, reference material, output verification, and responsible use, using examples relevant to the participants.
Experience applying AI to selected tasks and assessing the results.
Connected learning with time for practice between sessions
A connected program drawing on the training topics, with the number and length of sessions shaped around your audience, learning goals, and time for practice.
A shared foundation, practical experiments, and a scoped building exercise.
Hands-on exploration through a scoped prototype
Explore no-code automation, APIs, agents, and AI-assisted coding through a scoped prototype. Prerequisites and tool access are agreed before the session.
Experience turning an idea into a testable prototype and identifying its limits.
A tailored learning plan with room for depth and practice
Deeper coverage of the curriculum, with practical labs, structured experimentation, and an optional capstone shaped around the audience.
A learning sequence with room for practice, reflection, and feedback.
The teaching approach makes room for questions, shared discovery, and practical experiments. Participants compare outputs, test assumptions, work with relevant examples, and reflect on what should be verified or kept under human judgment.
Cautious optimism guides the work: explore what AI can make possible, understand where it can fail, and decide what is useful for the work and people involved.
Dr. Mohammad Keyhani is Associate Professor of Entrepreneurship and Innovation at Haskayne School of Business, University of Calgary. He designed and has taught the cross-campus Generative AI and Prompting course since 2023.
Its documented curriculum has seven core lectures, corresponding hands-on labs, and an AI-coding capstone. DigitVibe’s program draws on this teaching and curriculum, then adapts learning to organizational goals and participants’ work.
Offered independently by DigitVibe, without University of Calgary accreditation or endorsement.
Delivery is available virtually or in person. Topics, session count, session length, and overall program duration are agreed during scoping to suit your audience and learning goals.
Fees, tools, prerequisites, examples, and the facilitation team are determined together, based on your audience, objectives, setting, and practical constraints.
Tell us what your team needs