Hallucination Detection at the Trace Layer: 4 Detectors You Can Ship Today

Book: LLM Observability Pocket Guide: Picking the Right Tracing & Evals Tools for Your Team

Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go

My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools

Me: xgabriel.com | GitHub

You can't catch every hallucination at runtime. The cost of running a fact-check model inline doubles your p99 and burns budget on requests that were fine anyway. But you can catch four classes of hallucination cheaply, after the fact, in the trace pipeline. The detectors run on spans your app already emits. The user gets the answer at normal latency. The detector flags the bad ones a few seconds later and a Slack ping shows up before the support ticket does.