Primer series · mental models first
Demystify AI
A primer series for technical generalists: IT, security, ops, project managers, and anyone using tools daily who was never handed a working mental model for what happens under the hood.
Each piece starts with an almost-correct metaphor, refines it just enough to stay useful, then opens the next layer. Companion to Learn, with the same spine and a gentler register.
Start anywhere, but not nowhere
Primer catalog
Shorter pieces for building the vocabulary before deeper architecture decisions start to matter.
LLM construction stages, from pretraining to LoRA
A language model moves through stages: pretraining, supervised tuning, preference tuning, evaluation, serving, retrieval, and adapter training. LoRA enters as a compact adaptation layer after the expensive base model exists.
AI vs ML vs LLM vs agents — sorting out the words people keep mixing up
Four different words often collapse into one marketing pitch. A nested mental model makes the buying, building, and risk questions sharper.
What is MCP? The USB-C port for AI context
MCP is a standard way for an AI agent to ask another system for context or tools. Think less magic brain, more well-labeled port.
Tokens, context windows, attention — model mechanics without math
A working mental model for the path from prompt to returned text: tokens, context windows, and attention without a single equation.
Why LLMs hallucinate — same mechanism as the looseness, different consequence
Hallucination comes from the same retrieval looseness behind useful LLM answers, with a different consequence.
LLMs work like word-query databases, but looser
A practical mental model for LLMs: word-based queries over learned patterns, refined with the looseness behind iteration, useful surprises, and confident wrongness.