AI workflows

What is Prompt engineering? Definition & Context

Prompt engineering is the discipline of structuring instructions to language models — including context, constraints, and output format — to get reliably high-quality results.

Prompt engineering is the discipline of designing the instructions you send to a language model so its output is reliably good. It treats prompts as a real interface: there are conventions for structuring context, separating instructions from data, specifying output format, and constraining edge cases.

The fundamentals: explicit role, scoped task, relevant examples (few-shot), output schema, and clear failure handling. For UI work specifically, the prompts that actually ship are component-aware — they name the component, set the style, define the layout, list the must-haves, and specify responsiveness.

It is not magic phrasing. Modern frontier models will execute almost any reasonable instruction; the engineering is about making sure your instructions cover everything that matters before the model has to guess.

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Related terms

Vibe coding

Vibe coding is the practice of building software by describing intent to an AI in natural language and letting the AI write, refactor, and ship the code.

Agentic coding

Agentic coding is vibe coding done by an autonomous AI agent that reads context, plans steps, calls tools, and ships changes without you typing each instruction.

Context engineering

Context engineering is the practice of choosing exactly what information goes into the model's context window — files, snippets, specs — so the AI has the right knowledge without noise.

Pillar page

A pillar page is the long, evergreen guide at the center of a topic cluster — broad enough to rank for the head term, with internal links out to deeper supporting articles.