<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Evaluation on AI Knowledge Base</title><link>https://learn-ai.blindshot.kz/topics/evaluation/</link><description>Recent content in Evaluation on AI Knowledge Base</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://learn-ai.blindshot.kz/topics/evaluation/index.xml" rel="self" type="application/rss+xml"/><item><title>Building LLM Applications for Production</title><link>https://learn-ai.blindshot.kz/docs/ai-strategy/risk/technical-due-diligence/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://learn-ai.blindshot.kz/docs/ai-strategy/risk/technical-due-diligence/</guid><description>Comprehensive guide to production LLM challenges covering prompt engineering, evaluation, cost analysis, latency, fine-tuning vs prompting tradeoffs, and testing strategies.</description></item><item><title>Evaluating and Debugging Generative AI Models</title><link>https://learn-ai.blindshot.kz/courses/dlai-eval-debug-genai/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://learn-ai.blindshot.kz/courses/dlai-eval-debug-genai/</guid><description>&lt;p&gt;Covers evaluation metrics, debugging techniques, and systematic testing for generative AI applications using Weights &amp;amp; Biases. The practical companion to the Evaluation &amp;amp; Testing learning path — the course provides hands-on practice with evaluation tools, while the path covers the full evaluation landscape across providers.&lt;/p&gt;</description></item></channel></rss>