WRK-AIAP-1002 - AI at Work: From Blueprint to Build Mode
Course Description
This hands-on build workshop teaches participants how to create and refine a custom AI assistant designed to support real work they already do. Rather than focusing on a single workflow or industry, participants bring their own recurring task, workflow, communication challenge, process, or problem into the session. Using a guided build process, participants learn how to translate that work into a reusable AI-assisted workflow supported by a custom assistant configuration. The emphasis is not simply on building a tool, but on understanding the repeatable structures that make AI assistants more reliable, useful, and adaptable across day-to-day operational scenarios.
Participants define the assistant’s purpose, determine where AI can appropriately support the workflow, structure instructions and constraints, incorporate examples and source materials, and test outputs against real use cases. Throughout the workshop, participants learn how assistant behavior is shaped by context, prompting structure, examples, guardrails, and iteration.
The workshop emphasizes practical implementation over experimentation. Participants work directly inside AI platforms throughout the session in a guided studio environment focused on live building, testing, troubleshooting, and refinement. Facilitators provide hands-on support as participants iterate on assistant behavior, improve workflow alignment, resolve breakdowns, and refine configurations against real use cases in real time.
Participants build and refine assistants that can help create stronger starting points, improve consistency, reduce repetitive drafting or organizational work, and support more repeatable workflows while keeping people responsible for review, judgment, and final decisions.
Common workshop activities may include:
- Defining assistant roles and responsibilities
- Building workflow-specific instructions
- Creating reusable assistant interaction structures
- Organizing source materials and examples
- Troubleshooting vague or inconsistent outputs
- Improving reliability and clarity
- Identifying where human review must remain in place
- Testing assistants against real workflow scenarios
Participants leave with a working assistant they can begin using immediately, a reusable workflow structure tailored to recurring work they already perform, and a clearer understanding of how to continue refining AI-assisted work practices after the workshop.
Because this is a build-focused experience, participants should already have basic familiarity with AI tools and prompting concepts. Completion of “AI for Work: Practical AI for Real Workflows” is recommended but not required.
Core Principle: Assistants support the work — people remain responsible for the work. Participants learn how to build AI assistants that improve workflow support, consistency, and efficiency while keeping human expertise, judgment, and accountability central.
Human-in-the-Loop Principle: AI assistants are positioned as workflow support systems rather than autonomous decision-makers. Participants learn how to maintain human review, accountability, oversight, and decision-making throughout AI-assisted workflows.