MasterAI engineering.From prompt to production.
A free field guide to building with LLMs. Transformer internals you can actually picture, the full RAG-to-agents stack, and the production discipline — evals, cost, security — that most courses skip. All with diagrams and interactive animations.
The crash course
New to AI engineering? Start here. Fourteen focused modules that build the mental model — tokens, transformers, RAG, agents, evals — before you ship anything to real users.
The Prediction Loop: What Generative AI Actually Does
GenAI is one loop repeated: predict the next token. Understanding that loop explains the power, the cost model, and every major failure mode — including why models can't count R's.
Transformers Without the Math
Build the mental model every AI engineer needs: how tokens become vectors, why attention is quadratic, what the MLP layers store, and how depth stacks understanding.
Tokens, Costs, and the Context Window
Why text becomes tokens, how input/output pricing asymmetry drives your bill, what the context window actually limits, and how prompt caching cuts costs by up to 90%.
The Model Landscape: Choosing Without Getting Played
A decision framework for picking between closed frontier models, open-weight, and reasoning tiers — plus benchmark literacy so leaderboard scores stop misleading you.
Featured articles
The full production stack — RAG pipelines, agents, inference, fine-tuning, evals, guardrails — broken down. Every article is tagged by difficulty and ships with diagrams and interactive visualizers.
Sandboxing AI Agents: Containers, gVisor, and MicroVMs
Sandboxing AI agents with containers, gVisor, Firecracker microVMs, and Wasm: honest boot and overhead numbers, and why egress filtering matters most.
Mixture of Experts: why every frontier model went sparse
How mixture of experts works — routers, top-k selection, shared experts — and the VRAM and batching math that decides when sparse models save you money.
Code Mode: why agents write code instead of calling tools
Why agents that write code against tool APIs beat JSON tool calling: the 150K-to-2K token math, a worked before/after, and when direct calls still win.
Agent Skills: packaging expertise as files, not code
How Agent Skills use progressive disclosure to load expertise on demand — the SKILL.md format, real token math, and when to pick skills over MCP.
How AI coding assistants work
The engineering behind Copilot, Cursor, and Claude Code: repo maps, tree-sitter indexing, edit formats, long-file strategies, and how to evaluate code generation properly.
Long Context vs RAG: The Million-Token Question
When does stuffing a million tokens beat retrieval? The empirics on lost-in-the-middle accuracy, context-stuffing costs, and when RAG still wins at 1M tokens.
Browse by topic
Master the provider stack
OpenAI, Anthropic, Google, Meta, open-weight models — plus the tooling around them. Articles are tagged by the providers and frameworks they cover, so you can drill into the stack you actually use.
Build it. Eval it.
Ship it.
Whether you're making the jump into AI engineering or hardening an LLM product that's already in production — start here.