dspy-debugging-observability_skill

Overview

This skill helps debug, trace, and monitor DSPy programs in development and production. It combines quick inspection tools, MLflow autologging, and customizable callbacks to capture LLM calls, token usage, latency, and cost information. Use it to gain visibility into program execution, optimize prompt flows, and maintain production observability.

How this skill works

Start with inspect_history() to view recent execution traces stored by DSPy. Enable MLflow autologging to capture runs, token usage, costs, and timelines in an MLflow experiment. For production-grade monitoring, attach custom callbacks that receive lifecycle hooks (on_lm_start, on_lm_end) to aggregate metrics, sample traces, or push events to external systems.

When to use it

• Investigating unexpected LLM outputs or multi-step program flows
• Instrumenting production pipelines to track latency, cost, and errors
• Logging inference traces and token usage to MLflow for audits
• Building custom metrics or alerts with callbacks
• Sampling traces in high-volume services to limit overhead

Best practices

• Use inspect_history() for fast local debugging before enabling heavy tracing
• Enable MLflow autolog only where persistent trace storage and UI access are needed
• Implement sampling (1–10%) for high-traffic endpoints to balance visibility and cost
• Redact PII and sensitive fields inside callbacks before logging or exporting
• Run callbacks asynchronously or minimize work in hooks to avoid blocking LLM calls

Example use cases

• Quickly inspect the last program execution to see model, usage, and cost fields
• Autolog all RAG pipeline runs to MLflow to compare optimizer versions and prompt variants
• Attach a ProductionMonitoringCallback to report total cost, tokens, and error counts to a dashboard
• Use a SamplingCallback to store representative traces for forensic analysis without full logging
• Integrate callbacks with metrics systems (Prometheus, Datadog) to create alerts on latency or cost spikes