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Training is practice on the job, not a tour of the product
Train a team on ChatGPT with real work, shared briefs, and a review standard. A feature tour does not change behaviour. People leave a webinar able to describe custom instructions and still type make this better the next morning. Capability is a habit: the brief before the chat, the source beside the draft, the name on the send. You build that habit the way you build any workplace skill, by repeating it on artefacts the team already owns, in front of a standard the manager will actually use. If the workshop is a parade of clever prompts from the internet, you have entertained people. You have not trained them.
The cost of weak training is not only wasted hours in a meeting room. It is confidential paste, invented citations, and a quiet split between staff who invent private methods and staff who refuse the tool entirely. Both groups are responding to a vacuum. Fill the vacuum with a short curriculum: what may go in, what the default brief looks like, how review works, and which jobs are out of scope. Then practise. A team that has marked a bad draft together will recognise the next one. A team that only watched a demo will trust fluency because fluency was the demo.
This essay is a method for that curriculum. You will teach on live packs, freeze two or three briefs, rehearse the critique pass, and leave with a review checklist the manager samples. You will not try to cover every feature. Features change. The brief and the check do not. If you only have ninety minutes, spend them on one artefact and one standard. Depth beats a catalogue. The catalogue can live in a page people open when they need a reminder, not in a lecture they will not recall. Reminder pages are optional. Practice is not.
If the workshop does not use a real artefact the team will send this month, it is a product tour. Book practice instead, with the file in the room.
What a workplace ChatGPT curriculum actually contains
Four blocks are enough. First, data class and paste rules, with examples from your own work, not generic horror stories. Second, the brief: role, task, reader, facts, constraints, output, gaps. Third, the critique pass and the human checks for names, numbers, and promises. Fourth, the jobs you will not do in ChatGPT, including people judgements and ungrounded research. Teach those blocks with the team's artefacts on screen. Abstract slides about prompting produce abstract prompting. People copy what they rehearse. Rehearse the refusal as well as the draft, so no is a skill and not a mood.
Pair practice beats a hero at the front. Two people share one artefact: one writes the brief, the other attacks the draft with the checklist. Swap. The manager watches for paste risk and for generic tone, not for who types fastest. End by saving the surviving brief in a shared place with an owner. A workshop that produces no shared brief has produced nostalgia. Assessment is simple: can they brief, generate once, and catch three planted errors in a bad sample? If they cannot, you need another hour on the same material, not a new module on plugins.
| Block | Practice on | Done when |
|---|---|---|
| Data class | What this team must never paste | They stop a bad paste in the room |
| Brief | One live artefact they already own | The brief is saved with an owner |
| Critique | A draft with planted errors | They catch names, numbers, promises |
| Out of scope | A task the model must not take | They can say no without a debate |
Run three short practices, then sample
Session one is rules and a first brief on a low-risk internal artefact. Session two is the same brief on a harder pack, plus a planted-error draft they mark in silence before discussion. Session three is their own work in pairs, with the manager sampling two outputs against sources. Keep each session under ninety minutes. Between sessions they use the brief on real mail, not on toy prompts. If someone missed a session, they do not get a recording of a tour. They sit in on the next practice. Recordings of clicks do not create judgement.
After the third session, freeze the standard for a month. No new features, no prompt-of-the-week channel that undermines the brief. The manager samples weekly. When the same miss repeats, fix the brief in the shared note and say so. That is the training loop. Optional office hours can help stragglers. They must not become a second unofficial method. One default, one checklist, named coaches. That is how a team learns ChatGPT as work rather than as a hobby. Hobbies fragment quality. Work needs one method the queue will honour. Say so in the next stand-up.
Role: You are a training designer for a workplace team using ChatGPT.
Task: Turn my notes into a ninety-minute practice plan and a one-page standard.
Context: I will name the artefact, the data class, and the planted errors we will use.
Constraints:
- Do not add a feature tour or a plugin list.
- Include pair practice, a critique pass, and a manager sample.
- Mark missing facts in my notes instead of inventing a curriculum.
- Keep language plain enough to read aloud in the room.
Output: Timed agenda, the brief we will save, and the five review checks.
Quality checks: What in this plan would still leave people typing make this better?Leave the room with a saved brief, a checklist, and a date for the first manager sample. Send those three things the same day. If the only follow-up is a recording link, the training already failed. Practice continues on the artefacts, not in a learning system. When the sample finds a miss, teach that miss in the next stand-up in five minutes. That five minutes is more training than another webinar.
Tours, private genius, and training that never meets the manager
A tour trains people to hunt features. They will try plugins on confidential files because the demo made curiosity look like skill. Private genius is the next failure: one person builds a hidden prompt library and the rest of the team produces uneven work. Shared briefs exist to prevent that split. Training that never meets the manager is the third. Staff learn a standard in the workshop and a different standard in the queue. They follow the queue. Invite the manager to mark the planted errors with everyone else. If they will not, you do not have a standard. You have a class.
Do not grade people on how much they generate. Grade the brief and the catches. Do not run a certification quiz about model names. Run a practical: here is a bad draft and a source, find the problems. Refresh training when the data class or the default tool changes, not on a calendar that exists to spend a budget. Stale annual e-learning on AI awareness is how organisations pretend the habit is managed. Pretence does not catch a bad figure. A marked sample does, and it is shorter than a course.
| Mistake | What it looks like | What to do instead |
|---|---|---|
| Feature tour | Clicks and a recording | Live artefact and a saved brief |
| No standard | Each person invents a method | One brief, one checklist |
| Manager absent | Training vs the real queue | Manager marks the sample too |
| Volume prize | Who generated the most | Who caught the bad figure |
A workshop that never opens a real file trains confidence, not judgement. Book the file first, then the room, then the slides if you still need them.
Related reading on StudyGrid
Read next: Practice and Reference Managing People Who Use AI How to Write Better ChatGPT Prompts. Those essays sit beside this one. Use them when you need the neighbouring skill, not as a substitute for the check you still have to make.
What to do this week
Schedule three short practices on one artefact your team already sends. Write the paste rule, freeze a brief, plant three errors in a sample draft, and mark them together. Put the manager on the third session. Set a sample date ten days later. Keep only the brief that survived contact with the live work. Retire the rest of the prompt collection so it cannot undermine the default. Tell the team which brief is now the default.