<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>UX on English AI Terms Dictionary</title><link>https://terms-en.ai-term-hub.com/en/tags/ux/</link><description>Recent content in UX on English AI Terms Dictionary</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 18 Jul 2026 11:44:44 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/en/tags/ux/index.xml" rel="self" type="application/rss+xml"/><item><title>Prompt Engineering</title><link>https://terms-en.ai-term-hub.com/en/terms/prompt_engineering/</link><pubDate>Sat, 18 Jul 2026 07:38:16 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/en/terms/prompt_engineering/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>Prompt engineering involves crafting specific inputs, known as prompts, to elicit accurate, relevant, and high-quality responses from generative AI models. It requires understanding how models interpret context, instructions, and examples. Techniques include few-shot learning, chain-of-thought reasoning, and structured formatting. This discipline bridges human intent and machine capability, allowing users to maximize performance without modifying the underlying model weights. It is essential for developers integrating LLMs into applications to ensure reliability and consistency.&lt;/p></description></item></channel></rss>