<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bootstrap on Sergio A. Mora Pardo | AI/ML Technical Lead</title><link>https://sergiomorapardo.github.io/tags/bootstrap/</link><description>Recent content in Bootstrap on Sergio A. Mora Pardo | AI/ML Technical Lead</description><generator>Hugo</generator><language>en-US</language><atom:link href="https://sergiomorapardo.github.io/tags/bootstrap/index.xml" rel="self" type="application/rss+xml"/><item><title>Applied LLM Evaluation for Recommendations</title><link>https://sergiomorapardo.github.io/work/applied-llm-evaluation/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://sergiomorapardo.github.io/work/applied-llm-evaluation/</guid><description>&lt;h2 id="the-problem"&gt;The problem&lt;/h2&gt;&#10;&lt;p&gt;LLM prototypes can appear persuasive without being reliable. For personalized recommendations, the team needed an evaluation method that could separate candidate-retrieval quality from generation quality and make regressions visible before a live experiment.&lt;/p&gt;&#10;&lt;h2 id="my-role"&gt;My role&lt;/h2&gt;&#10;&lt;p&gt;I led applied research on LLM systems for personalized recommendations and established the team’s initial offline evaluation methodology. I also guided the design of an MCP-based tool layer for supplying recommendation context to LLM-generated notifications.&lt;/p&gt;</description></item></channel></rss>