monitoring

Monitoring AI workflows involves instrumenting tracing, metrics, and logs across your system. This Skill automates end-to-end monitoring by integrating Langfuse tracing for LLM calls and token tracking, Prometheus metrics for performance collection, and Grafana dashboards for visualization and alerting, giving you complete observability of RAG pipelines and structured logging with correlation IDs.

Yes. Langfuse tracing captures LLM calls, token usage, and cost by logging spans with start/end traces and structured output. This Skill integrates Langfuse to provide detailed traceability of each LLM interaction in your workflows, making it easy to audit performance and costs.

Define Prometheus counters, histograms, and gauges to collect metrics from your services, then visualize them in Grafana dashboards. This Skill guides you through configuring alert rules in Grafana to proactively detect issues, enabling real-time monitoring of system behavior in cloud-native deployments.

Structured logging organizes logs in machine-readable formats with metadata like correlation IDs to link related events across services. This Skill implements structured logging with correlation ID management, making it faster to trace issues through distributed systems and understand the full context of failures.

Yes. This Skill is designed for monitoring end-to-end RAG pipelines, LLM services, and microservice architectures. It supports traceability and metric collection across multiple services in cloud-native deployments, with alert configuration to catch issues before they impact users.