The method

Why practice beats a course.

Most AI training for employees is a video course. People watch it, nod, and go back to using AI the way they did before, because watching somebody else be competent has never made anybody competent. The thing that transfers is doing the work and being corrected while you do it. That is the whole method, and everything below is how it is built.

Nearly every company has now given its staff an AI tool. Far fewer have made their staff good at using one, and those are completely different problems. A licence is a purchase. A skill is a practice. Handing someone a chatbot and a policy document does not produce the second one, which is why so much AI spend shows up on an invoice and nowhere else.

The gap is measurable, and it is not a gap in enthusiasm.


These are findings about AI and AI training in general, from primary sources, each linked. None of them is a measurement of Trainwork learners: we have not run that study, and we will not present somebody else's result as if we had.

One caveat worth stating, because it is the kind usually left out: the Quarterly Journal of Economics study varied whether workers had access to an AI assistant, not whether they were trained. It is honest to say the least experienced gained the most, since the assistant worked by surfacing expert practice to everyone. It would not be honest to claim it measured trained against untrained workers.


Not a criticism of video as a format. It is a good format for explaining a thing. It is a poor one for building a skill, and AI at work is entirely a skill.

A recorded AI course against a coached Trainwork conversation, on six axes.
 A recorded AI courseA coached conversation
Who does the workYou watch someone else do itYou do it, and get corrected while you do
Fit to your jobOne fixed path, whatever your job isThe conversation adapts to your role and your industry
The examplesExamples from a company that is not yoursYour own work is the example, every time
When you find out it did not landWeeks later, at your deskIn the session, while it can still be fixed
What you leave withNotes, and a completion tickThe thing you came in needing
Shelf lifeOut of date the quarter after it is recordedCoached against how the tools behave this month

A single session teaches a trick. Three sessions in the same skill, shaped differently, teach the skill. The shape is the same everywhere in the curriculum.

01

Foundation

Do it once, with the reasoning made explicit

The first conversation does not tell you a rule and move on. It makes you build the thing, names why each part works while you are building it, and refuses to hand you the finished answer. Being told the four parts of a good prompt is worth very little. Assembling one for a message you actually have to send is worth a great deal.

02

Advanced

Do it again when it goes wrong

The second conversation starts from the failure case, because that is where the skill really lives. A prompt that works is easy. Knowing what to change when a good prompt still returns something useless is the part that separates someone who uses AI from someone who gets value out of it.

03

Mastery

Do it a third time, and keep the artifact

The third conversation ends with something reusable that goes into your notes: a prompt library, a template, a checklist you wrote. Practice that leaves nothing behind decays. Practice that leaves a tool behind gets used again next week, which is the only retention mechanism that has ever reliably worked.

15 skills at 3 conversations each is 45 reps, and finishing them earns the Fluency Certificate, which carries a public verification page anyone can check. See the full curriculum.

Last reviewed . Every figure on this site links to its source.