LLM observability for production apps: what to trace, measure, and fix
LLM observability starts with the whole request
LLM observability is the practice of connecting a user request to the model calls, retrieval steps, tool calls, latency, cost, and final outcome that followed. In production, logging the generated text alone is not enough. When an answer is slow, expensive, unsafe, or simply wrong, builders need to see which step caused the failure.
The practical approach is to create one trace for the application request and attach a span to every meaningful operation: prompt construction, retrieval, model invocation, tool authorization, tool execution, validation, and response delivery. Record measurements that help you make a decision, but do not automatically store sensitive prompts or user data.
This is an excerpt. Read the full post at otf-kit.dev/blog/llm-observability-guide — full-stack kits your AI coding agent can actually ship to production. Browse the kits →
