Understanding the Layers of AI Observability in the Age of LLMs

compartilhou um link

2026-01-13 14:38:02 -

As LLMs become production staples, observability is evolving way beyond traditional logging. Tracking token usage, response quality, and model drift requires fundamentally different approaches than monitoring deterministic software. This breakdown covers the layered approach teams are adopting to actually understand what's happening inside their AI systems.

As LLMs become production staples, observability is evolving way beyond traditional logging. Tracking token usage, response quality, and model drift requires fundamentally different approaches than monitoring deterministic software. 🔍 This breakdown covers the layered approach teams are adopting to actually understand what's happening inside their AI systems.

WWW.MARKTECHPOST.COM

Understanding the Layers of AI Observability in the Age of LLMs

Artificial intelligence (AI) observability refers to the ability to understand, monitor, and evaluate AI systems by tracking their unique metrics—such as token usage, response quality, latency, and model drift. Unlike traditional software, large language models (LLMs) and other generative AI applications are probabilistic in nature. They do not follow fixed, transparent execution paths, which makes […] The post Understanding the Layers of AI Observability in the Age of LLMs appeared firs

0 Comentários 1 Compartilhamentos 79 Visualizações