<p><strong>Short answer</strong></p> <p><strong>LLM observability is runtime visibility into an LLM or agent system: the traces, metrics, and logs that let you see what a model and its agent loop actually did on a given request, so failures are diagnosable in production rather th…
<h2> The first trace looks the same everywhere </h2> <p>Wrap your LLM client with any open-source observability SDK — Langfuse, Helicone, Opik, Phoenix, doesn't matter which — and the first result is identical: a request goes out, a span shows up in a dashboard with the prompt, t…
dev.to — LLM tag
TIER_1English(EN)·Aniket Abhishek Soni·
<p>Six months ago, debugging our RAG pipeline meant staring at a wall of unstructured CloudWatch logs, trying to figure out which chunk of a 50-page PDF caused the hallucination. It was a digital scavenger hunt where the clues disappeared as soon as the request finished. Today, I…
<h2> The box both of them check </h2> <p>If you're adding your first bit of visibility into an LLM app, the simplest version is a <code>print()</code> statement before the API call. That's enough while you're the only one testing it.</p> <p>The natural next step is to swap that p…
