Alloy
Prompting, Context Engineering & Structured OutputsComing

Prompting, Context Engineering & Structured Outputs

How to drive a language model deliberately — the difference between asking nicely and actually engineering a prompt and its context window so the model returns output you can parse and act on every time. You leave able to design prompts and context that produce reliable, machine-readable results instead of prose you have to scrape.

What you'll learn

  • Distinguish prompt tweaking (fiddling with wording until it looks better) from context engineering (deliberately deciding what information enters the window, in what order, with what framing) — and explain why the second is what scales.
  • Lay out a context window on purpose: instructions, grounding evidence, examples, and the task, ordered so the model attends to the right things.
  • Design a prompt that produces structured output — JSON or another machine-parseable shape — that parses reliably rather than occasionally.
  • Recognise the common failure modes (prose wrapped around JSON, hallucinated fields, silent format drift) and the techniques that prevent each one.

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