How to Coach AI

Impact on

Your Organisation

Higher quality AI outputs – When employees know how to guide, review and improve AI-generated work, outputs become more relevant, useful and fit for purpose.
More confident and responsible use of AI – By understanding where AI adds value and where human judgement is essential, employees are less likely to accept weak outputs or over-rely on the technology.
Greater efficiency and consistency – Teams spend less time starting again or rewriting disappointing outputs and can capture what works for future tasks.

Workshop Participants

Understand why prompting is only the beginning and what it means to coach AI-supported work.
Learn how to brief AI clearly, provide useful context and agree what good looks like.
Practise reviewing AI output and giving feedback that improves the result.
Identify what learning to use now, save for next time or build into a repeatable workflow.

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More Information

Most AI guidance focuses on the prompt: finding the right words to get a good response. Prompting still matters, but it is only the beginning. Even a strong prompt cannot guarantee that the first output will be accurate, relevant or genuinely useful.

Using AI well means managing the work from a brief to the finished output. Just as you would with a capable colleague, you need to explain the purpose, provide relevant context, set expectations, review what comes back and give clear feedback. Human judgement remains essential because AI cannot take responsibility for deciding whether its work is good enough for your audience, organisation or purpose.

In this session, participants use the practical COACH framework to Clarify the job, Offer useful context, Agree what good looks like, Critique and coach the work, and Harvest the learning. Through discussion and practical activities, they apply the approach to everyday tasks such as writing, planning, analysis and meeting preparation.

Participants leave with practical ways to improve the work they produce with AI and a clearer sense of what feedback is useful only now, what should be saved for next time and what could be built into a repeatable workflow.

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