<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Prompt-Engineering on Elliot Belt</title><link>https://felixbillieres.github.io/tags/prompt-engineering/</link><description>Recent content in Prompt-Engineering on Elliot Belt</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>felix.billieres@ecole2600.com (Elliot Belt)</managingEditor><webMaster>felix.billieres@ecole2600.com (Elliot Belt)</webMaster><copyright>© 2026 Elliot Belt</copyright><lastBuildDate>Thu, 07 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://felixbillieres.github.io/tags/prompt-engineering/index.xml" rel="self" type="application/rss+xml"/><item><title>Prompting for Security Research: How to Build Prompts That Actually Find Vulnerabilities</title><link>https://felixbillieres.github.io/posts/prompting-for-cybersecurity-2026/</link><pubDate>Thu, 07 May 2026 00:00:00 +0000</pubDate><author>felix.billieres@ecole2600.com (Elliot Belt)</author><guid>https://felixbillieres.github.io/posts/prompting-for-cybersecurity-2026/</guid><description>Most people use LLMs for security wrong. They ask &amp;lsquo;find all bugs&amp;rsquo; and get noise. This article breaks down the empirical research behind what actually works: structured prompting, adversarial self-verification, CWE-specialized chains, context engineering, and the full composite prompt template that gets you from noise to actionable findings. With numbers.</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://felixbillieres.github.io/posts/prompting-for-cybersecurity-2026/featured.png"/></item></channel></rss>