LLM Observability
What to monitor in AI workflows: prompts, retrieval, tool calls, traces, latency, cost, quality, and business outcomes.
Direct answer
LLM observability is the practice of tracing and monitoring AI system behavior across prompts, model responses, retrieval, tool calls, latency, cost, errors, and quality signals.
What gets monitored
A normal application log can tell you that a request failed. It usually cannot tell you which retrieved source changed the answer, which prompt version was active, which tool call failed, or whether quality regressed after a model swap.
LLM observability makes AI behavior inspectable enough for a team to debug and improve it.
- Model inputs, outputs, prompt versions, and structured responses.
- Retrieved chunks, source documents, scores, filters, and citations.
- Tool calls, arguments, returned values, and failures.
- Latency, token usage, cost, and model routing.
- User feedback, review decisions, and business outcome labels.
Why are normal logs not enough for AI apps?
AI systems depend on prompts, retrieval, model behavior, and tool calls. Without tracing those steps, teams cannot reliably explain or debug bad outputs.
What is a trace in an AI workflow?
A trace is a step-by-step record of a request, including model calls, retrieval calls, tool calls, timing, metadata, and errors.
What metrics matter most?
Start with latency, cost, error rate, retrieval quality, groundedness, tool-call success, review acceptance, and the business outcome the workflow exists to improve.
Want to know what a build like this would involve?
The first deliverable of every engagement is a scoped build plan — integration map, what to automate first, and a fixed number.
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