ai-tracing-requests

Per-step tracing lets you inspect individual LM calls, intermediate results, and timings within a single AI request to pinpoint exactly why a wrong response was produced. This provides structured visibility into complex pipelines for precise debugging.

Profiling slow AI pipelines is done by running a trace on a specific request to view per-step latencies and intermediate results. This reveals exactly which LM calls or pipeline stages are causing delays, enabling targeted performance optimization.

OpenTelemetry instrumentation supports production-grade AI request auditing by providing end-to-end traces of customer interactions. It enables back-end integration and compliance tracking by capturing detailed per-step execution data for post-mortem analysis.

Exporting AI request traces to JSONL format creates structured audit trails for compliance and post-mortem analysis. This allows you to capture and store the complete per-step execution history of requests for later inspection and regulatory review.

DSPy inspection integrates directly with per-step tracing to easily inspect and reproduce a failing AI request. This combination lets you view the exact LM calls and intermediate results needed to understand and replicate failures in complex flows.

End-to-end tracing is needed for AI debugging when you must understand the exact sequence of LM calls, intermediate results, and timings within a single request. It provides deeper visibility than standard logging for auditing complex multi-step pipelines.