AI logging in production: context without oversharing
Instrument AI workflows with useful metadata, configuration versions, and clear outcomes—not entire conversations.
AI observability
Understand the work around the model.
Connect application workflows, individual model calls, token measurements, and evaluation results. These guides take a metadata-first approach and distinguish observed outcomes from estimates and assumptions.
Your reading path.
Start with workflow instrumentation, add correlation across stages, then examine usage attribution. Keep retries, partial streams, and missing measurements visible throughout the investigation.
LLM observability: connect logs, traces, and outcomes
Trace retrieval, generation, validation, and retries without confusing model completion with application success.
Token usage logging: measurements, estimates, and cost
Keep reported usage separate from estimates, handle retries, and make cost calculations explainable.
ONE EVENT IS A GOOD START
Make your next log more useful.
Start with a clear schema. Keep the context that matters.