<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Llm on Commentary of Takao</title><link>https://takao.blog/ko/tags/llm/</link><description>Recent content in Llm on Commentary of Takao</description><generator>Hugo -- gohugo.io</generator><language>ko</language><copyright>Commentary of Takao</copyright><lastBuildDate>Sun, 12 Jul 2026 04:12:51 +0900</lastBuildDate><atom:link href="https://takao.blog/ko/tags/llm/index.xml" rel="self" type="application/rss+xml"/><item><title>LLM Prompt Engineering: A Developer's Practical Guide</title><link>https://takao.blog/ko/web/llm-prompt-engineering/</link><pubDate>Tue, 30 Apr 2024 00:00:00 +0900</pubDate><guid>https://takao.blog/ko/web/llm-prompt-engineering/</guid><description>&lt;img src="https://takao.blog/img/thumbnail/llm-prompt-engineering-ko.png" alt="Featured image of post LLM Prompt Engineering: A Developer's Practical Guide" /&gt;&lt;p&gt;Prompt engineering has become an essential skill for developers building applications with large language models. As LLMs integrate deeper into software products, effective prompt design directly impacts output quality, reliability, and cost. This article provides a practical, developer-focused 가이드 to prompt engineering techniques that work in production.&lt;/p&gt;
&lt;h2 id="prompt-structure-fundamentals"&gt;Prompt Structure Fundamentals
&lt;/h2&gt;&lt;p&gt;A well-structured prompt follows a consistent architecture: system message (behavior and persona), context (background information), task description (what the model should do), examples (few-shot demonstrations), input (the actual data), and output format (expected response structure).&lt;/p&gt;</description></item></channel></rss>