Wires Langfuse tracing into LLM apps for production observability, monitoring, telemetry, and offline eval - instruments via `@observe` (Python) / `startActiveObservation` (TS) decorators that auto-capture inputs / outputs / timings / errors per generation; exposes `langfuse.update_current_span()` for metadata + cost / latency annotation; supports trace-bound scoring for eval datasets and prompt-as-code management. Use when the user needs to monitor, log, trace, or debug LLM API calls in production beyond pre-deploy eval, wants to add LLM observability tooling to an existing app, or wants to ship traces from production to an eval dataset for offline regression testing.