<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Production on XiDao Tech Blog</title><link>https://blog.xidao.online/en/tags/production/</link><description>Recent content in Production on XiDao Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>© 2026 XiDao</copyright><lastBuildDate>Fri, 01 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.xidao.online/en/tags/production/index.xml" rel="self" type="application/rss+xml"/><item><title>10 Hard Lessons from Production AI API Calls in 2026</title><link>https://blog.xidao.online/en/posts/2026-ai-api-production-lessons/</link><pubDate>Fri, 01 May 2026 00:00:00 +0000</pubDate><guid>https://blog.xidao.online/en/posts/2026-ai-api-production-lessons/</guid><description>&lt;h2 class="relative group"&gt;Introduction
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&lt;p&gt;In 2026, large language models are deeply embedded in production systems across every industry. From Claude 4 Opus to GPT-5 Turbo, from Gemini 2.5 Pro to DeepSeek-V4, developers have an unprecedented selection of models at their fingertips. But calling these AI APIs in production is nothing like a quick notebook experiment.&lt;/p&gt;
&lt;p&gt;This article distills 10 hard-earned lessons from real production incidents. Each one comes with a war story, a solution, and runnable code. Hopefully you won&amp;rsquo;t have to learn these the hard way.&lt;/p&gt;</description></item></channel></rss>