Support after AI training: what to agree before booking

A Syntalith program can include agreed independent practice between sessions and a later discussion. The proposal should explain what work participants will return to and how feedback will help them use what they learned. Sessions, materials, and any assessment after a period of practice are scoped explicitly. The word training does not imply continuous instructor availability.

What to bring back to the instructor

A concrete attempt provides a useful basis for discussion: its purpose, source material, and the point where the author got stuck. It might be a document comparison with an overly general conclusion or a response that misses the customer's question. The instructor can help identify missing context, an unsuitable source, or a writing difficulty.

Support like this develops work begun in the program: describing the task, selecting information, using an approved tool, and assessing the result. With ChatGPT Work or Claude Cowork, retain material approved for discussion and an explanation of your choices. There is no need to collect an entire work history for a question about one passage.

An account failure, access change, or unclear company rule needs a different contact. IT or the process owner knows those arrangements. Participants should know whom to approach before the sessions. A training instructor does not automatically become the application's technical support team.

A new case reveals a different question

Suppose a participant practiced comparing two short proposals and later receives one with an attachment. Their summary of the main document is accurate, but it omits service coverage described in the attachment. During review, they revisit material selection before editing the prose. They can then independently compare another set of documents. That is a useful continuation of practice; replaying the original presentation would leave the question unresolved.

CIPD's guidance on learning needs describes ongoing work with stakeholders and updating the understanding of required capabilities. Follow-up can draw on questions that emerge only during actual tasks.

Match the format to the difficulty

A shared discussion makes sense when several people encounter a similar task. An individual conversation may suit one demanding document. A recurring question about a company rule belongs somewhere the team can find the answer again. Another training meeting is not necessary for every difficulty.

Agree on the subjects, how work will be shared, and when the company wants to collect questions. The proposal specifies the number of reviews and the response format. New materials or a different task can lead to a scope discussion once the need is understood.

The manager makes room to use the approach, while a designated person gathers recurring questions. Experienced AI users may also need feedback on a harder case. Beginners need time for their own attempt before someone completes the work for them. Subsequent independent work can show which explanations helped and what still needs practice.

Describe the questions employees brought back after their last course. We can discuss suitable follow-up practice and how help is shared. See our pricing page for information about working together.