A hands-on workshop where analysts, operators, researchers, and leaders learn to plan, build, debug, and responsibly use Claude Code on real work.
Best for people who understand the work deeply and need a practical way to prototype, automate, and evaluate AI-assisted building.
People who already understand the work and need a faster way to turn ideas into small tools, dashboards, and repeatable workflows.
Teams with a backlog of internal process fixes, prototypes, intake flows, and reporting helpers that never quite reach the top of engineering's list.
Leaders who need practical fluency in AI coding agents before making broad adoption or vendor decisions.
Founder/operator groups that need a concrete way to test ideas, build demos, and understand what should move to a professional build later.
Faculty, staff, and research teams that want to build useful tools without pretending everyone is becoming a full-time software engineer.
People close enough to the work to supervise AI output, but who need a repeatable workflow for planning, debugging, and review.
Scope the right first version, write a useful implementation plan, and avoid asking Claude to build a vague idea.
Give Claude Code clear context, constraints, acceptance checks, and iteration instructions.
Review structure, spot obvious risks, and know when to ask for an explanation or a rewrite.
Use errors, logs, browser output, and small tests to steer Claude when the build breaks.
Deploy or demo a small working artifact instead of leaving with notes and screenshots.
Understand privacy, security, shadow-IT, and quality boundaries before using AI coding agents broadly.
The format depends on whether your goal is leadership education, team fluency, or a real internal pilot.
A practical leadership session on what Claude Code can do, where it fails, and how to structure a safe pilot.
A focused team session covering setup, workflow, prompting patterns, and one guided prototype or internal use case.
The core hands-on workshop: participants scope, plan, build, debug, and demo a working prototype with Claude Code.
Training plus follow-up: select use cases, build between sessions, review outputs, and leave with a practical adoption playbook.
The workshop centers on a concrete build loop: idea, plan, design, build, debug, and ship.
Participants learn how to supervise Claude Code and make sound product/workflow decisions, not memorize syntax.
We talk directly about data privacy, tool access, account setup, failure modes, and when the work needs professional engineering.
Stephen uses Claude Code for prototypes, workflows, client work, and production apps, so the class focuses on what actually works.
The team version builds on the same workshop tested with early attendees: small groups, setup help, real building, and a workflow people can keep using after the day ends.
The corporate version adapts that experience for teams, departments, leadership groups, and organizations evaluating Claude Code adoption.
I can now seamlessly plan, build, and deploy with a trusted workflow.
Dr. Hill encouraged me in a way that takes away the intimidation of being a beginner and seeing a pathway toward being only limited by imagination and time.
Taking a course that made me confident I can stay ahead of the curve took AI integration from uncomfortable to capable.
Tell us who would attend, what they want to build, and whether you're thinking briefing, workshop, or pilot sprint.