Lesson 1191 of 1550
AI Observability Engineer Trace Design: Instrumenting LLM Calls That Tell a Story
AI can draft an AI observability trace schema and span attributes, but the production instrumentation and PII handling decisions are the engineer's.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2observability
- 3tracing
- 4span attributes
Concept cluster
Terms to connect while reading
Section 1
The premise
AI can draft an AI observability trace schema with span attributes that capture model, prompt class, tool calls, tokens, latency, and outcome.
What AI does well here
- Produce a span attribute table with name, type, cardinality risk, and PII flag
- Draft a sampling policy that preserves rare-error visibility
What AI cannot do
- Verify that the implementation honors the PII flags at runtime
- Decide what observability data may cross trust boundaries
Key terms in this lesson
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