← MLOps & ML Engineering / 11

mediumMicrosoft Databricks OpenAI

How do you build observability for an LLM application, and how does it differ from traditional monitoring?

You cannot improve an LLM app you cannot see into, and LLM observability is not service monitoring. The signal is tracing multi-step chains, capturing inputs/outputs/tokens/cost, and online quality signals, not just latency and errors.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

You cannot improve an LLM app you cannot see into, and LLM observability is not service monitoring. The signal is tracing multi-step chains, capturing inputs/outputs/tokens/cost, and online quality signals, not just latency and errors.

more free answers with an account · no card

UP NEXT ON YOUR JOURNEY

Next in this trackHow do prompt caching and semantic caching cut LLM cost and latency, and what are the risks?Next in this trackHow does LLMOps differ from traditional MLOps, and how do you version and manage prompts in production?Next in this trackWhat is LLM routing (model cascades / semantic routing), and how do you implement it?

DISCUSSION · 0

No comments yet — be the first to share your approach.