AI and automation are reshaping every job in tech — and the people who understand them are the ones who thrive. This library teaches modern AI from the ground up: how large language models work, how to prompt them, how to build with RAG and agents, and how to automate real work safely and responsibly. Learn it once here and apply it across every role.
The AI Landscape
How AI, ML, deep learning and generative AI nest together.
Tap or hover a part to learn more.
The broad field.
The widest circle: any software that performs tasks usually needing human intelligence — reasoning, perception, language. Everything below is a subset of AI.
Check your understanding
1. How do AI and machine learning relate?
2. What does generative AI do?
Keep learning
What this path covers
- AI Fundamentals — AI, machine learning, deep learning and generative AI.
- Large Language Models — how models like GPT and Claude actually work.
- Prompt Engineering — getting reliable results from AI.
- AI Assistants — ChatGPT, Claude, Gemini and Microsoft Copilot.
- RAG — grounding AI in your own data.
- Vector Databases — embeddings and semantic search.
- AI Agents — AI that plans and takes actions.
- Automation — turning manual work into workflows.
- Python for AI Automation — the language of AI.
- AI Productivity — working faster and smarter with AI.
- AI Security — the risks of AI systems.
- AI Governance — using AI responsibly and legally.
- AI in Cyber Security — AI on attack and defence.
Where this fits
Pair this with Programming Fundamentals and Cyber Security Fundamentals, then rehearse with AI Interview™ and evidence it in your Career Passport™.
Start with AI Fundamentals.