Topics

Offline eval scores are green and production is worse. The gap is not a measurement error — it is structural. Here is how to instrument online evaluation so production quality becomes observable.

RAG systems fail at the embedding and index layer long before the LLM does. Here is what to actually monitor: embedding drift, index staleness, recall decay, and retrieval quality in production.

Production LLM apps span multiple model calls, tool invocations, retrieval steps, and re-tries. A complete trace makes them debuggable; a sparse one leaves you guessing.

ML Observe covers ML observability and MLOps from a production-engineering perspective. Here's what we publish.

An honest breakdown of the three open-source tools most teams reach for — what problem each was built for, where they overlap, where they don't, and how to assemble them without buying a platform you don't need yet.