Free self-paced course

LLM Foundations Course

This free LLM foundations course explains how language models turn text into tokens, predict the next token, use a finite context window, and generate with decoding controls. It also teaches prompting, evidence, citations, abstention, and the difference between fluent output and verified knowledge.

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For

Beginners, product professionals, and developers who want an accurate LLM mental model before implementation details.

Study time

8–12 hours

Prerequisites

None. The course begins with visible examples and introduces vocabulary before code or notation.

What you will learn

  • Trace the next-token generation loop.
  • Inspect tokenization, context, temperature, and top-p.
  • Write a bounded prompt and output contract.
  • Evaluate grounded and unsupported answers.

Recommended starting guides

Use these answer-first lessons to build the concepts this course depends on.

See how LLMs generate text, token by tokenLearn how transformer attention and residual streams workBuild a grounded RAG pipeline with citations and evaluation

Course format

Each topic combines a concise explanation, an animated mechanism, hands-on evidence, and an optional book-level deep dive.

  • Six focused foundation lessons
  • Five interactive LLM mechanisms
  • Intuition, Builder, and Systems lenses
  • LLM Behaviour Lab capstone
  • Free glossary and comparison guides

Mastery evidence

LLM Behaviour Lab

Run controlled experiments across tokenization, decoding, prompting, and grounding, then write an evidence-based behaviour brief.

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