Module 1: Introduction to Generative AI

• What is Generative AI and how it differs from traditional AI
• Evolution of AI: from rule-based systems to foundation models
• Key use cases across industries: content, code, images, and data
• Overview of major GenAI platforms and tools

Module 2: Large Language Models (LLMs)

• How LLMs work: transformers, attention mechanisms, and tokenisation
• Pre-training, fine-tuning, and RLHF explained
• Overview of GPT-4, Claude, Gemini, Llama, and Mistral
• Understanding model parameters, context windows, and inference

Module 3: Prompt Engineering Basics

• Principles of effective prompting
• Zero-shot, few-shot, and chain-of-thought prompting
• Structuring prompts for consistent outputs
• Common prompt patterns and anti-patterns

Module 4: Generative AI for Text and Content

• Text generation, summarisation, and translation use cases
• AI writing assistants and content automation workflows
• Retrieval-Augmented Generation (RAG) overview
• Evaluating and validating AI-generated content

Module 5: Generative AI for Images and Multimodal Models

• How image generation models work: diffusion models explained
• Tools: DALL-E, Midjourney, and Stable Diffusion overview
• Multimodal models: processing text, images, and audio together
• Practical applications in design, marketing, and media

Module 6: Building Simple GenAI Applications

• Introduction to OpenAI API and Anthropic API
• Building a simple chatbot with LLM APIs
• Connecting GenAI to external data with RAG basics
• Introduction to LangChain for application development

Module 7: Responsible AI and Ethics

• Bias, hallucination, and limitations of GenAI systems
• AI safety principles and responsible deployment
• Data privacy, copyright, and intellectual property concerns
• AI governance frameworks and organisational policies

Module 8: Hands-on Project and Assessment

• Building a complete GenAI-powered mini application
• Prompt design and output evaluation exercise
• Real-world use case analysis and business case writing
• Final assessment and course certification

Experience in the Industry Gain knowledge from AI practitioners who have deployed large language models and generative AI solutions across enterprise environments.

Backgrounds at the Top Our Generative AI trainers have worked with leading technology firms including Google, Microsoft, OpenAI ecosystem partners, and top AI-first startups.

Clear & Effective Teaching Complex transformer architectures, prompt engineering, and RAG pipelines are broken down into practical, beginner-friendly concepts.

Hands-On Learning Focus Students build real GenAI applications using OpenAI API, LangChain, and vector databases through structured hands-on labs.

Up-to-Date Knowledge Trainers stay current with the rapidly evolving generative AI landscape including latest LLM releases, frameworks, and responsible AI guidelines.