Professional Certificate for Generative AI & Large Language Models

Course Introduction

Designed for professionals in Artificial Intelligence & Data Science, this five-day professional-certificate course builds practical command of Generative AI & Large Language Models. Every session pairs a clear concept with a workplace exercise, so learning turns into capability rather than notes.

Training Objectives

• 01Describe how Generative AI & Large Language Models is defined, governed and measured in international practice
• 02Work through typical situations in data, analytics and automation initiatives using structured Generative AI & Large Language Models techniques
• 03Recognise common risks, errors and non-conformities linked to Generative AI & Large Language Models — and how to prevent them
• 04Choose the right indicators and records to monitor Generative AI & Large Language Models over time
• 05Translate the course learning into a realistic plan agreed with your line manager

What You Will Take Away

• A practical architecture or workflow blueprint
• A risk and controls checklist for your environment
• Sample datasets, scripts or configurations to reuse

Who Should Attend

• Business & Digital Transformation Leaders
• Data & Analytics Professionals
• IT & Innovation Managers
• Operations Managers Exploring AI

Prerequisites: Suitable for practising professionals; basic familiarity with the subject is helpful.

Training Methodology

Each day pairs structured teaching with practical application and a short knowledge check, giving a clear record of progress. Case material and examples are drawn from data, analytics and automation initiatives.

Day-by-Day Programme (5 Days)

• Day 1
  Getting oriented: the landscape of Generative AI & Large Language Models

  Scope, terminology and the standards and regulations that shape Generative AI & Large Language Models in data, analytics and automation initiatives.

• Day 2
  Core concepts and methods

  The principles, codes and best-practice approaches that leading organisations rely on for Generative AI & Large Language Models.

• Day 3
  Applying it on the job

  Hands-on exercises using tools and templates for Generative AI & Large Language Models, built around realistic situations in data, analytics and automation initiatives.

• Day 4
  Cases, risks and lessons learned

  Case studies, typical pitfalls and the risk and compliance issues that arise with Generative AI & Large Language Models.

• Day 5
  Bringing it together

  Consolidation workshop, personal action planning and a closing knowledge check on Generative AI & Large Language Models.

Upcoming Sessions