Built to teach.

Why this exists

Most AI courses die at the demo. They teach you to build a notebook RAG pipeline, then stop — right before the parts that decide whether your system survives contact with real users: evaluation, cost control, observability, security, and the failure modes nobody mentions in the launch tweet.

Ironclad Academy covers the internals and the production layer, and teaches both visually — every big idea ships with an animated, interactive visualizer you can step through.

What's inside

• Crash Course — fourteen modules that build the mental model: what LLMs actually do, transformers without the math, tokens and costs, RAG, agents, evals, guardrails, and LLMOps. Read these first if you're new.
• Deep-dive articles — focused explanations across the whole stack: prompt and context engineering, embeddings and vector databases, advanced RAG, agent architectures and MCP, reasoning models, fine-tuning, inference and serving, evaluation and observability, safety and security, voice and multimodal.
• Interactive visualizers — step through a BPE tokenizer, reshape a sampling distribution with temperature, watch attention weights light up, walk a document through a RAG pipeline, and run an agent loop one step at a time.

Tagged for your level

Every article is tagged by difficulty so you can pick what fits:

• Beginner — assumes minimal background, explains every term.
• Intermediate — assumes you can call an API and read Python; builds the engineering judgment.
• Advanced — assumes you've shipped the basics; dives into trade-offs, internals, and edge cases.