SkillShaft
Generative AIBeginner · 5 weeks

Generative AI Foundations

Build a solid understanding of modern Generative AI and the ecosystem around it.

Before you can use AI effectively, you need to understand it. This programme builds a genuine, practical understanding of Generative AI — what it is, how it works, what it can do, and where it's heading. You won't be writing code, but you will leave with the mental models and vocabulary to navigate the AI landscape with confidence.

Self-Paced

Prerequisites

  • —No technical background required
  • —Genuine curiosity about AI
  • —Willingness to engage with conceptual material

What you'll be able to do

  • ✓Explain what Generative AI is and how it differs from traditional software
  • ✓Understand how large language models work at a conceptual level
  • ✓Navigate the modern AI ecosystem with confidence — knowing the key players and tools
  • ✓Use prompting techniques that get dramatically better results from AI systems
  • ✓Understand the implications of multimodal AI, agents, and autonomous systems
  • ✓Apply responsible AI principles in everyday use
  • ✓Talk intelligently about AI in professional and academic contexts

Curriculum

1. What is AI?

  • ·A brief, honest history of AI
  • ·Narrow AI vs. General AI: what we actually have today
  • ·How AI is different from traditional software and rule-based systems
  • ·Why Generative AI is a genuine paradigm shift

2. Machine Learning Overview

  • ·What machine learning actually is, in plain English
  • ·Supervised, unsupervised, and reinforcement learning
  • ·How models learn from data
  • ·Why ML is the foundation under most modern AI

3. Generative AI

  • ·What makes AI 'generative'
  • ·Text, image, audio, and video generation
  • ·How Generative AI is being applied across industries
  • ·Current capabilities and real limitations

4. Large Language Models

  • ·What an LLM is and how it was trained
  • ·The transformer architecture — without the maths
  • ·Why scale matters and what 'emergent capabilities' means
  • ·Key models: GPT, Claude, Gemini, Llama, and others

5. Tokens and Context

  • ·What tokens are and why they matter
  • ·Context windows: what AI can 'remember'
  • ·Why context management is a critical skill
  • ·Practical implications for prompt design

6. Prompting

  • ·Why prompting is a genuine skill
  • ·Core prompting techniques: zero-shot, few-shot, chain-of-thought
  • ·Instruction, context, examples, and constraints
  • ·Practical prompting for everyday tasks

7. Multimodal AI

  • ·AI that sees, hears, and generates images
  • ·GPT-4o, Gemini, and Claude's multimodal capabilities
  • ·Practical use cases for multimodal AI
  • ·Image generation: Midjourney, DALL-E, Stable Diffusion overview

8. AI Agents Introduction

  • ·What an AI agent is
  • ·Tools, memory, and planning in agent systems
  • ·Simple vs. complex agent architectures
  • ·Real-world examples of AI agents at work

9. Responsible AI

  • ·Hallucinations, bias, and why AI gets things wrong
  • ·Privacy, data security, and what you shouldn't share
  • ·The ethics of using AI-generated content
  • ·Navigating AI in a world of regulation and uncertainty

10. The Modern AI Ecosystem

  • ·Key companies, models, and tools in 2025
  • ·Open-source vs. closed models
  • ·APIs, fine-tuning, and how AI gets deployed
  • ·What to watch: trends and where AI is heading

Format & Duration

Duration

5 weeks

Format

Self-Paced

Level

Beginner