<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Evals on AI Tools Daily - Automate Your Work with AI</title><link>https://aitoolsdaily.org/tags/evals/</link><description>Recent content in Evals on AI Tools Daily - Automate Your Work with AI</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Wed, 22 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://aitoolsdaily.org/tags/evals/index.xml" rel="self" type="application/rss+xml"/><item><title>LangSmith Review 2026: The LLM Observability Tool Teams Actually Trust</title><link>https://aitoolsdaily.org/langsmith-review-2026-the-llm-observability-tool-teams-actually-trust/</link><pubDate>Wed, 22 Apr 2026 00:00:00 +0000</pubDate><guid>https://aitoolsdaily.org/langsmith-review-2026-the-llm-observability-tool-teams-actually-trust/</guid><description>LangSmith Review 2026: The LLM Observability Tool Teams Actually Trust If you&amp;rsquo;ve ever debugged an LLM application by adding print statements, watching streams of JSON in your terminal, and trying to remember which prompt version produced which output, you understand why LLM observability tools exist. LangSmith is the category&amp;rsquo;s most mature option in 2026.
I&amp;rsquo;ve used LangSmith on three production AI products for over a year. Here&amp;rsquo;s the honest assessment.</description></item></channel></rss>