I Built LLM Explained: 43 Interactive Pages That Take You From Tokens to AI Agents

Description and use

The author built LLM Explained, a 43‑page interactive course that teaches large language model fundamentals through animations, depth‑switchable explanations, and hands‑on widgets.

Problem

Learners struggle to understand large language model concepts because new terminology and complex equations appear rapidly, making it hard to grasp why certain design choices (e.g., scaling by √d_k) are made.

Outcome

The course enables learners to understand LLM concepts through visual, interactive, and hands‑on methods, reducing confusion around terminology and mathematical details.

Workflow

• 1. Visit the curriculum map to see the course structure.
• 2. Choose a module and select a depth level (Intuition, Mechanics, Maths).
• 3. Scroll to trigger step‑by‑step animations and use play/pause controls.
• 4. Interact with widgets (e.g., toggle √d_k, parameter counter, BPE tokenizer).
• 5. Run optional in‑browser Python cells to reproduce figures.
• 6. Read the “what this picture leaves out” box for honest limits.

Evidence

I built the resource I wished I had: LLM Explained. It’s a free, interactive course that goes from "why did RNNs hit a wall?" all the way to "how does a production agent retrieve, reason, act and get evaluated?"

Source

Every concept appears as a picture you can play with before you see its equation.

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Every module has at least one widget you control.

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Every animation also has a text transcript, and if your system has reduced motion turned on, animations turn into numbered static frames.

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