AI logging in production: context without oversharing
Instrument AI workflows with useful metadata, configuration versions, and clear outcomes—not entire conversations.
TOPIC COLLECTION
AI observability
Connect the operational stages around a model call. Read about metadata, configuration versions, trace relationships, and usage provenance without treating prompt retention as the default.
Read it. Then test it.
Use this collection to choose a focused design question. Each guide includes a practical investigation or fixture exercise, a primary technical reference, and links to the next relevant pattern. Start with the event you need to understand, then validate the full path from producer to reader.
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.