What will your team work on?
A proposed sequence, adapted to your tools, experience and available time.
Understand the assistant
Distinguish generating an answer from finding a verified fact. Compare plausible text with a response grounded in a provided document.
Write a usable request
Specify the task, context, audience, constraints and expected format. Add an example when the output must follow a standard.
Spot the failure
Look for unsupported statements, invented references and missing caveats. Ask what would let a colleague verify the result.
Set everyday boundaries
Identify approved tools and prohibited data. Practise on fictional or authorised examples and agree how to escalate uncertainty.
What does practice look like?
A first prompt library and a review checklist
Summarise a fictional policy into five employee questions. Compare the answers with the policy and label every detail the assistant added without a source.
A teaching example. It does not describe a completed client engagement.
| Work product | What makes it usable |
|---|---|
| Source material | Approved inputs, with the useful context retained. |
| Reusable instructions | A clear task, constraints and expected output format. |
| Review checklist | The checks a colleague must perform before using the result. |
| Next experiment | An owner, an example and criteria for deciding whether to continue. |
What should you bring?
Bring a concrete question, a representative example and the rules your team already follows. The purpose of preparation is to spend the session practising, with the right accounts, permissions and source material in place.
- A task that someone on the team does regularly.
- An approved AI tool, or a decision about which environment to use.
- A sample that can be shared safely, preferably stripped of sensitive data.
- A reviewer who knows what a good result should look like.
The final proposal sets the duration, group size, trainer, scope and fee. Tool subscriptions and any follow-up are specified separately.
