Foundation program

Generative AI Training for Business Teams

Build practical AI literacy with hands-on exercises in writing, summarization, research and evaluation. A foundation for technical and non-technical teams.

Discuss this training

The practical answer

What does this program cover?

Generative AI training helps employees understand how AI creates text and other outputs, where those outputs can fail and how to use an approved tool for a clearly defined task. NMR Infotech combines plain-language concepts with guided practice and human review.

Audience

Who is this training for?

Employees starting with AI, managers planning team adoption and specialists who need a common foundation before tool-specific training. Technical experience is not required for the introductory path.

Adaptable curriculum

What will participants learn?

Module 1

Understand the output

Distinguish generating a plausible response from retrieving a verified fact. Explore how context, instructions and missing information affect the answer.

Module 2

Choose a suitable task

Identify bounded drafting, summarization and organization tasks. Separate these from decisions requiring accountable professional judgment.

Module 3

Provide useful context

Write a task brief with permitted facts, an audience, constraints and a usable output format. Keep unapproved information outside the exercise.

Module 4

Review before use

Check claims against the source, preserve uncertainty and identify fabricated details. Learn when to revise a prompt and when to stop.

Module 5

Build a repeatable habit

Turn one successful exercise into a documented workflow with an input boundary, named reviewer and clear handoff.

Hands-on examples

What will the team practise?

Summarize a fictional meeting

Use notes containing a confirmed action, an unresolved issue and a deferred decision. Compare the AI summary with the notes and correct any invented owner or deadline.

Explain a process to a new colleague

Provide a short, synthetic process description. Ask for an explanation and a checklist, then verify that exceptions and approval steps remain intact.

These are proposed learning exercises using synthetic or permitted inputs, not client case studies or claims of measured outcomes.

Learning checks

What should participants be able to demonstrate?

Explain common AI capabilities and limitations in ordinary business language.
Select a suitable starting workflow and recognize when a task is outside its scope.
Write a bounded instruction and review the result against permitted source material.
Document what a colleague needs to repeat the task responsibly.

Plan the session

How is the program tailored?

Share the participants’ roles, experience, approved tools and two or three recurring tasks. We can then discuss a suitable briefing, workshop or longer learning path, with delivery online, on-site or hybrid subject to agreed scope and arrangements.

The proposed curriculum is a starting point. Duration, prerequisites, software access, materials, follow-up support and fees are agreed in the engagement brief. Participants should have access to the selected tool; any licensing or administrator requirements need confirmation before the session.

Questions about this program

Is this a programming or machine-learning course?

The foundation focuses on practical business use. Coding, model development or technical implementation can be scoped separately for an appropriate audience.

Can we use our own documents in an exercise?

Yes, where the organization has explicitly approved both the material and the tool used to process it. Public or synthetic examples provide an alternative when that approval is not available.

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