AI

Techniques for getting more out of AI coding agents and large language models, and an account of what those models actually are. Most of the section is practitioner-facing — how to make a model running in a loop produce correct work reliably and economically — and the last two entries go the other way, taking the machinery apart.

The section shares one running example: using Claude Code to write a page for this wiki. It is a real agentic workload over artifacts in this repository, so each topic below can point at the same concrete task instead of inventing a fresh hypothetical.

  • Context engineering — managing what the model sees: the context budget, curation, compaction, sub-agent isolation, and the pitfalls that cause wrong actions.
    • Claude Code — those techniques applied end-to-end in a real harness; the section’s worked example.
  • Prompt engineering — wording the instructions the model receives.
  • Prompt caching & cost — the token economics behind latency and spend.
  • Agentic workflows — planning, tool use, delegation, and verification loops.
  • Agentic engineering — the lifecycle around the loop: evaluation, observability, guardrails, and cost engineering.
  • AI plugins — bundled extensions to an agentic harness: what they are, when to install one, and when to author your own.
  • MCP — the open protocol underneath most plugin tool surfaces.
  • Neural networks — the other direction, and the foundation of it: what a fitted model is made of, why each part is there, and how training fills in the blanks. Architecture-neutral.
  • Large language models — those parts arranged into a transformer: what a model actually is, from a token going in to a guess coming out, plus how it was trained and what it costs to run.
Backward Cashing Out Context Engineering Forward