<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Software Testing on The Coders Blog</title><link>https://thecodersblog.com/tag/software-testing/</link><description>Recent content in Software Testing on The Coders Blog</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 29 Apr 2026 17:04:21 +0000</lastBuildDate><atom:link href="https://thecodersblog.com/tag/software-testing/index.xml" rel="self" type="application/rss+xml"/><item><title>Engineering Predictability: Why LLM Determinism is the Next Frontier in AI Development [2026]</title><link>https://thecodersblog.com/a-new-benchmark-for-testing-llms-for-deterministic-outputs-2026/</link><pubDate>Wed, 29 Apr 2026 17:04:21 +0000</pubDate><guid>https://thecodersblog.com/a-new-benchmark-for-testing-llms-for-deterministic-outputs-2026/</guid><description>&lt;p&gt;Your LLMs might be silently corrupting your enterprise data. Producing perfectly valid JSON with hallucinated values isn&amp;rsquo;t just a nuance; it&amp;rsquo;s a critical flaw that&amp;rsquo;s holding back true AI adoption in production. This isn&amp;rsquo;t theoretical fear-mongering. We&amp;rsquo;re talking about the silent erosion of data integrity, the kind that costs millions in remediation and opportunity.&lt;/p&gt;
&lt;p&gt;For too long, the AI community has celebrated models that &lt;em&gt;mostly&lt;/em&gt; work, or produce outputs that are &lt;em&gt;almost&lt;/em&gt; right. This permissiveness has been a necessary evil in the rapid development of LLMs. However, as these powerful systems move from experimental labs to the core of enterprise operations, &amp;ldquo;almost correct&amp;rdquo; becomes an unacceptable liability. It&amp;rsquo;s time to demand more.&lt;/p&gt;</description></item></channel></rss>