<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Vector Search on E7Coding</title><link>https://www.e7coding.com/en/tags/vector-search/</link><description>Recent content in Vector Search on E7Coding</description><generator>Hugo</generator><language>en</language><managingEditor>Joy</managingEditor><webMaster>Joy</webMaster><lastBuildDate>Tue, 16 Jun 2026 14:00:00 +0800</lastBuildDate><atom:link href="https://www.e7coding.com/en/tags/vector-search/index.xml" rel="self" type="application/rss+xml"/><item><title>Understanding Recall and Recall Strategies: A Core Layer in Search, Recommendation, and RAG</title><link>https://www.e7coding.com/en/posts/ai-recall-and-recall-strategy/</link><pubDate>Tue, 16 Jun 2026 14:00:00 +0800</pubDate><author>Joy</author><guid>https://www.e7coding.com/en/posts/ai-recall-and-recall-strategy/</guid><description>&lt;blockquote&gt;
&lt;p&gt;In AI systems, &lt;strong&gt;recall&lt;/strong&gt; and &lt;strong&gt;recall strategies&lt;/strong&gt; appear in almost every scenario where you need to find things from data: search, recommendation, RAG, Q&amp;amp;A, risk detection, object detection, information extraction, and more. They answer one shared question: &lt;strong&gt;can the system find as many of the relevant things as possible?&lt;/strong&gt;&lt;/p&gt;</description></item></channel></rss>