Executive summary
LLM observability connects prompts, retrieval, model responses, tool calls, latency, token use, errors, traces, and evaluation outcomes across one experience.
Method
Applied synthesis of observability concepts and evaluation practices for Generative AI workflows.
What the evidence suggests
Monitoring and evaluation differ
Monitoring shows operational state; evaluation examines relevance, correctness, grounding, safety, and usefulness.
Trace the complete journey
A final answer alone may not reveal whether the failure began in retrieval, a tool, orchestration, or generation.
Start with material risks
A small set of decision-relevant signals is more useful than collecting everything without an action plan.
Practical test protocol
- 01Define critical user journeys and risk categories.
- 02Connect request, retrieval, tools, output, timing, and evaluation evidence.
- 03Set alerts for operational and quality regressions.
- 04Review patterns and evolve thresholds after releases.
Logs + Traces + Metrics + Evaluations create a more complete quality signal.
Limitations
Observability does not automatically establish correctness; measurements require governance, access controls, and interpretation.