Corporate Generative AI Training
for Your Teams

Who it is for

Engineers, product and platform people who will build and maintain AI features — the team that will still own the system after the training ends.

Prerequisites

Working software development experience. Participants should be comfortable reading and writing code in a language their team already uses; no prior machine-learning background is assumed. Exercises can run against representative material — production access is not required to attend.

Learning objectives

• Design an agent or retrieval feature and explain why that shape was chosen
• Build a retrieval pipeline and diagnose whether a bad answer is a retrieval problem or a model problem
• Write an evaluation set that catches a regression before it reaches users
• Scope and cost an AI feature against real constraints, including deciding not to build it

Topics covered

• How LLMs work at the level a practitioner needs — tokens, context, confabulation
• Prompting for professional, repeatable output
• Agents and multi-step automations
• RAG — retrieval design, chunking, and when not to use it
• Connecting models to existing data and tools
• AI-assisted development in your own repositories
• Evaluation sets and regression checks
• Failure modes: context, memory, silent model changes
• Scoping and costing an AI feature

Who it is for

Business, finance and operations people who will use AI in their own work rather than build it — analysts, reporting and planning roles, and the managers who review their output.

Prerequisites

No programming background assumed. Participants should be fluent in their own analysis or reporting work; the programme applies AI to that work rather than teaching the work itself.

Learning objectives

• Write structured prompts — task, context, constraints and output format — that produce usable, checkable business output
• Draft and review workplace documents against a four-point gate: accuracy, tone, compliance and context
• Check an AI summary for omissions and invented detail before it informs a decision
• Run research synthesis with AI tools and verify the claims against sources
• Tell a grounded, document-based answer from an ungrounded one, and verify before acting on either

Topics covered

• Structured prompting: the four ingredients, role and context, iterative refinement
• Prompt patterns — summarise, compare, rewrite, classify, critique — and the prompt audit
• Workplace writing: structured email drafting, summary fidelity, report scaffolds with verification placeholders
• Research synthesis and source verification; citation practice with AI assistance
• Presentation and visual content, and reviewing it for professionalism and accuracy
• Document-grounded question answering, and the limits of retrieval
• Handling sensitive data in prompts — placeholders rather than real records
• Where AI improves a business process, and where it does not

Who it is for

Leadership and executive teams deciding where AI belongs in the P&L — sponsors who fund AI work and are asked to judge it, rather than people who will implement it.

Prerequisites

None technical. Participants need familiarity with their own cost base and business functions; there is no implementation work in this programme.

Learning objectives

• Map AI opportunities across your own business functions and rank them
• Apply a build-vs-buy-vs-wait framework to a specific proposal on your desk
• Interrogate a vendor proposal on total cost of ownership rather than on a demo
• Set governance and risk expectations proportionate to what is actually being deployed

Topics covered

• AI opportunity mapping across business functions
• Build vs. buy vs. wait, as a decision framework
• Real cost and ROI — TCO, vendor evaluation, budgeting
• Governance and risk, scoped to what the organisation is deploying