Prompt engineering is the craft of wording an instruction to a language model so it produces the desired output reliably. Where context engineering decides what the model sees, prompt engineering decides how that instruction is phrased — role and system prompts, few-shot examples, chain-of-thought elicitation, output-format constraints, and the failure modes that come from ambiguous or over-stuffed prompts.

For a concrete instance — how the rules and the page request are worded for an agent that then acts on them — see the section’s running example, Claude Code: writing a page for this wiki.

This page is a stub. In the meantime:

• Anthropic — Prompt engineering overview
• Anthropic — Use examples (multishot prompting)
• Wikipedia — Prompt engineering
• OpenAI — Prompt engineering guide