<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Search</title><link>https://blog.ast.moe/en/tags/search/</link><description>Recent posts: Gorimuchu</description><generator>Hugo</generator><item><title>How to Choose Between LLM, Embedding Models, Jev, and Classical NLP</title><link>https://blog.ast.moe/en/blog/2026-09-26-3/</link><pubDate>Sat, 26 Sep 2026 20:30:00 +0900</pubDate><guid>https://blog.ast.moe/en/blog/2026-09-26-3/</guid><description>&lt;p&gt;I often see people treating search, classification, comparison, and judgment as tasks that can simply be handed off to an LLM. If you ask it in natural language, you get a response, and you can keep asking follow‑up questions directly. That convenience is indeed useful, and I’m prone to entrusting everything to it. As a result, it’s easy to develop an impression that an LLM can solve any problem.&lt;/p&gt;
&lt;p&gt;On the other hand, even when using a judgment model like Jev, you can still give it tasks that aren’t suited for it. Asking it to hunt for information before the necessary data has been gathered, or to have it formulating an overall strategy before any candidates have been identified, is asking a limited‑purpose decision‑making tool to perform investigative work as well. If you only need to locate known terms, you can simply perform a string search without invoking a model at all.&lt;/p&gt;</description></item></channel></rss>