All Topics
Start here, grasp the essentials
Foundations
Before jumping into advanced AI systems, you need a solid mental model of how language models actually work, what embeddings are, and how AI processes information. These are the building blocks everything else on this platform is built on.
Lessons
1
How Language Models Actually Work
Demystify LLMs from tokens to next-word prediction, using simple analogies you will actually remember.
2
How Transformers Work
The architecture behind every modern LLM. Understand Self-Attention, multi-head attention, and why Transformers changed everything.
3
What Are Tokens?
Tokens are the atomic units LLMs read and write. Learn why tokenisation matters for cost, context limits, and model behaviour.
4
The Context Window Explained
Why LLMs forget, what context limits mean in practice, and how to design around them.
5
Embeddings: Meaning as Numbers
Understand how text gets converted into vectors and why the distance between those vectors is what makes semantic search possible.
6
Training vs Inference
Two completely different operations under one hood. Know the difference to understand cost, speed, and why models can't just 'learn' your data on the fly.
7
Prompt Engineering Basics
Craft prompts that get consistent, high-quality results from any language model.
8
Why AI Hallucinates (and What to Do About It)
Understand the root cause of hallucinations and the architectural patterns that reduce them significantly.
9
Why Models Need External Knowledge
Training data is frozen. The world is not. Discover why retrieval, tools, and context injection exist and when each is the right solution.
10
Tool Calling Fundamentals
How LLMs invoke external functions, what a tool definition looks like, and how the model decides when and how to call one.