Implementing AI Observability: Tracing LLM Calls End-to-End

Automated excerpt

Classic application observability covers latency, error rate, and throughput. A decorator-based tracer that writes structured JSON gives you enough to start. prices = COST_PER_1K. get(model, {"input": 0. 001, "output": 0. 003}) elapsed = (time. perf_counter() - start) * 1000 Every call now emits a structured JSON line. The security hardening checklists at AYI NEDJIMI include an LLM application security checklist that covers observability requirements alongside prompt injection and output validation controls — worth running through before you go to production.

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