<?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 Ted Factory</title><link>https://tedfactory.com/en/tags/llm/</link><description>Recent content in LLM on Ted Factory</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 02 Mar 2026 15:55:29 +0900</lastBuildDate><atom:link href="https://tedfactory.com/en/tags/llm/index.xml" rel="self" type="application/rss+xml"/><item><title>Key AI Concepts (1): AI / Machine Learning / Deep Learning / LLMs</title><link>https://tedfactory.com/en/books/ai-for-startup/ai-concepts-1/</link><pubDate>Sat, 27 Dec 2025 00:00:00 +0900</pubDate><guid>https://tedfactory.com/en/books/ai-for-startup/ai-concepts-1/</guid><description>&lt;h1 id="key-ai-concepts-1"&gt;Key AI Concepts (1)&lt;a class="anchor" href="#key-ai-concepts-1"&gt;#&lt;/a&gt;&lt;/h1&gt;
&lt;p&gt;To use AI effectively, it’s important to understand the key concepts behind it. This helps you understand how AI services and tools work, and why the latest news and trends matter.&lt;/p&gt;
&lt;p&gt;AI is still evolving at a rapid pace. Services and tools are continuously updated, and new developments emerge all the time. If you build a solid foundation, you can follow the changes more easily and keep improving your ability to apply AI in practice.&lt;/p&gt;</description></item><item><title>Key AI Concepts (2): Fine-tuning / RAG / Function Calling / MCP</title><link>https://tedfactory.com/en/books/ai-for-startup/ai-concepts-2/</link><pubDate>Fri, 02 Jan 2026 00:00:00 +0900</pubDate><guid>https://tedfactory.com/en/books/ai-for-startup/ai-concepts-2/</guid><description>&lt;h1 id="key-ai-concepts-2"&gt;Key AI Concepts (2)&lt;a class="anchor" href="#key-ai-concepts-2"&gt;#&lt;/a&gt;&lt;/h1&gt;
&lt;p&gt;In this chapter, we cover key concepts you’ll frequently encounter when turning a pre-trained LLM into a real product: fine-tuning, RAG, function calling, and MCP.&lt;/p&gt;
&lt;hr&gt;
&lt;h2 id="fine-tuning"&gt;Fine-tuning&lt;a class="anchor" href="#fine-tuning"&gt;#&lt;/a&gt;&lt;/h2&gt;
&lt;p&gt;Fine-tuning is the process of further training a pre-trained model to better fit a specific purpose. Thanks to open-source culture, training datasets and pre-trained models are often publicly available, and you can fine-tune them to improve performance or specialize in a particular domain.&lt;/p&gt;</description></item></channel></rss>