https://blog.pytoshka.me/en/tags/llm-orchestration/Recent content in Llm Orchestration on pyToshka's DevSecOps BlogHugoen-USping@pytoshka.me (pyToshka)ping@pytoshka.me (pyToshka)Fri, 29 May 2026 00:28:56 +0400https://blog.pytoshka.me/en/post/the-judge-pattern-llm-verdicts/Fri, 22 May 2026 00:00:00 +0000ping@pytoshka.me (pyToshka)https://blog.pytoshka.me/en/post/the-judge-pattern-llm-verdicts/<p>When a large language model returns a confident verdict - &ldquo;this document is fraudulent&rdquo;, &ldquo;this alert is benign&rdquo;, &ldquo;this transaction is safe&rdquo; - the natural instinct is to trust it. The model wrote a fluent rationale, cited the right fields, and reached a clean conclusion. The problem is that fluency is not correctness. In a low-stakes setting, an occasional wrong answer is noise. In a high-stakes pipeline, where a single verdict can deny someone a job, close a critical security incident, or release a fraudulent payment, the cost of a confident wrong answer is real and asymmetric.</p>