LLM observability and tracing
LLM applications are multi-step systems. A bad answer may come from the prompt, model, retrieval, reranker, tool result, parser, or product policy. Tracing makes that path visible.
The trace
A useful trace records:
- User input and request metadata.
- System prompt and assembled context.
- Retrieved chunks and scores.
- Model calls, parameters, tokens, latency, and cost.
- Tool calls, arguments, results, and errors.
- Final response and post-processing.
- Eval/judge scores and human feedback.
That trace is the debugging unit. Without it, you are guessing from the final answer.
The five pillars for LLM apps
| Pillar | Question |
|---|---|
| Quality | Did the system answer correctly? |
| Latency | Where did time go? |
| Cost | Which step spent tokens or compute? |
| Safety | Did policy or guardrails trigger? |
| Reliability | Which component failed or retried? |
Trace replay
Good observability lets you replay production traces against a new prompt, model, or retrieval config before release. That turns real traffic into regression tests.
Pitfall
Logging only final prompts can leak sensitive data and still miss the failure. Log structured traces with redaction, retention policy, and access control.
Connects to: assembling context · tool calls · evals