<?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 BumbleB Technologies</title><link>https://bumbleb.co/tags/llm/</link><description>Recent content in Llm on BumbleB Technologies</description><generator>Hugo</generator><language>en-US</language><copyright>© 2024-2026 BumbleB Technologies Pvt. Ltd.</copyright><lastBuildDate>Mon, 06 Jul 2026 15:40:22 +0530</lastBuildDate><atom:link href="https://bumbleb.co/tags/llm/index.xml" rel="self" type="application/rss+xml"/><item><title>Wrapping vs. Extracting</title><link>https://bumbleb.co/blog/2026-07-06-wrapping-vs-extracting/</link><pubDate>Mon, 06 Jul 2026 08:00:00 +0530</pubDate><guid>https://bumbleb.co/blog/2026-07-06-wrapping-vs-extracting/</guid><description>&lt;h1 id="wrapping-vs-extracting">Wrapping vs. Extracting&lt;/h1>
&lt;h2 id="why-most-ai-products-die-the-moment-the-model-gets-smarter--and-which-ones-survive">Why most AI products die the moment the model gets smarter — and which ones survive&lt;/h2>
&lt;p>There is a particular disappointment that arrives a few weeks after the demo. The tool was impressive in the room. It answered the questions everyone asked, phrased them well, made the team feel faster. Then the real work began, and something quieter set in: point a model at your data and it nails the obvious questions, then stalls on the ones you actually needed answered. The magic was real. It just stopped exactly where the value started.&lt;/p></description></item></channel></rss>