You shipped your LLM-powered feature. It worked great in testing. Then users started reporting hallucinations, inconsistent outputs, and responses that completely ignored their instructions. Sound familiar?

I've been there. Three times in the last year alone. The problem isn't that LLMs are unreliable — it's that most of us are flying blind once our AI features hit production. We don't have the observability, evaluation, or guardrail infrastructure that we'd never skip for a traditional backend service.

Let me walk through how I stopped guessing and started actually debugging my LLM applications.

The Root Cause: You Can't Debug What You Can't See

With a traditional API, debugging is straightforward. You check logs, look at status codes, trace the request through your system. With LLM applications, the failure mode is completely different.

Your API returned a 200. The response was valid JSON. The model even sounded confident.…