LLM Observability
Inspecting prompts, retrievals, tool calls, model choices, latency, cost, and outputs in production.
What is LLM Observability?
LLM observability is what turns a black box into an operable system. Without it, every failure looks like “the agent did something strange.” See AI Agent Observability: Logs, Traces, Audit.
What is an example of LLM Observability?
After a release, answer quality drops for Swedish documents. Traces show that a routing change sent those requests to a model with weaker Swedish performance, while retrieval quality remained stable.
How is LLM Observability different from related concepts?
Monitoring reports known metrics and alerts. Observability provides enough connected evidence to investigate failures that were not predicted in advance.
What should enterprises evaluate for LLM Observability?
- Adopt a trace model that spans the user request, retriever, reranker, model, tools, policies, approvals, and final outcome.
- Track task success and groundedness alongside infrastructure metrics such as latency and error rate.
- Redact secrets and sensitive fields while preserving the evidence needed for debugging and audit.
Read the full guide: LLM Observability — in-depth article →
Putting LLM Observability to work?
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