Originally published at mlopslab.org/llm-observability — updated weekly. 0 sponsors, 0 affiliate links. ⚡ Quick answer: LLM observability is the practice of collecting metrics, traces, and logs from large language model applications to monitor behavior, catch failures, control costs, and improve output quality — in real time. Unlike traditional APM, it handles non-deterministic outputs, prompt/response pairs, token costs, hallucination rates, and multi-step agent chains that standard monitoring tools were never built for. Table of Contents LLM observability: the actual definition Why traditional APM fails for LLMs Why it matters in 2026 The three pillars: metrics, traces, logs Key LLM observability metrics Best LLM observability tools (2026) How to implement it in Python — step by step RAG observability: what's different Common mistakes to avoid FAQ 1.…