AI & Generative AI
Learn AI Fundamentals
AI fundamentals covers the vocabulary and mental models behind modern AI: what machine learning is, what a large language model does, and what these systems can and cannot reliably do.
Why teams use it
- Without the basics, AI features get built on wrong assumptions about reliability.
- Knowing how tokens and context work explains most cost and quality behaviour.
- It lets you tell apart problems AI suits from problems ordinary code solves better.
Where you will meet it
Deciding whether a support feature needs a language model or a well-written database query is a judgement that starts with understanding both.
What to learn, and in what order
Work through these roughly in order. Jumping to the advanced list before the basics are solid is the most common way people get stuck.
Beginner
- AI, machine learning and deep learning
- What generative AI produces
- Large language models in plain terms
- Tokens, context and limits
Intermediate
- Embeddings and similarity
- Where models are strong and weak
- Evaluation basics
Advanced
- Cost and latency trade-offs
- Failure modes and mitigation
Have a project worth talking through?
Tell us what you're building or what's slowing your current system down. We'll give you a direct read on scope and approach.