ML Primitive Decoder

Table of Contents

1. Workflow
2. The Primitive Catalog
3. Decoding Patterns
4. Ablation Thought Experiment
5. Common Patterns
6. Guardrails
7. Quick Reference

Most ML constructs that look opaque are actually a short stack of well-understood linear algebra primitives composed in a specific way. Attention is dot product + softmax + weighted sum. Layer norm is recenter + rescale + learnable affine. Diffusion is noise injection + denoising regression repeated. This skill names the primitives, then shows why their composition produces the behavior the construct is famous for.

The signature deliverable is: construct = primitive₁ + primitive₂ + primitive₃, and that's why it does X.

Quick example (Attention):