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AI Training

AI Hackathon and Practical Workshop

Not a taught session: every participant builds their own use case, on their own work, with a trainer in the room. Best run once staff have seen what the tools can do.

Carried overfrom a document supplied by the client, taken 7 September 2026 and not rewritten. Source URL not supplied.

1 x 90-Minute Hands-On Workshop (online or in person)

Not a taught session: every participant builds their own use case, on their own work, with a trainer in the room. Best run once staff have seen what the tools can do.

Summary:

A working session rather than a lesson. There is no new subject and nothing to sit through: each participant picks a real task from their own week, the one that genuinely eats their time, and builds the thing that takes it off their plate, with a trainer helping them at the keyboard. Anyone who arrives without an idea is given a prompt that interviews them about their role, their tasks and the data they handle, and hands back around fifteen concrete use cases to choose from, so nobody is left staring at a blank screen. Finished builds are shown to the room as they land, which is what sparks the next round.

Course Breakdown:

  • How the session runs
  • The two ways in: bring a use case, or have the AI find one for you
  • The idea generator: a prompt that interviews the participant, then returns about fifteen use cases matched to the tools they actually have
  • Choosing the right one, with the trainer if needed
  • Putting it on the board so every person in the room gets time
  • The build loop: build it, test it, fix it, with support throughout
  • The decision framework: whether the task wants a prompt, a template, a scheduled prompt, a notebook or an agent
  • Show and tell: working builds demonstrated to the room as they succeed, then straight on to the next one
  • Where each build goes next, and how it becomes an agent that runs on its own

Outcome:

Participants leave with something they built themselves and can use the next morning, plus a repeatable method for finding the next one. Because every build comes out of the person’s own workload, the session doubles as a survey of where AI would genuinely save the organisation time, and it is the natural bridge into agent training, where those same builds are automated.