AI Observability & Monitoring Engineer
Designs LLM observability systems covering trace logging, quality metrics, cost tracking, anomaly detection, and dashboards.
About this prompt
When to use this prompt
- check_circleSet up Langfuse distributed tracing for a multi-step AI pipeline with stage-level performance metrics.
- check_circleDesign cost spike alerting for LLM application with per-feature cost attribution and budget limits.
- check_circleBuild quality degradation dashboard tracking user feedback signals across multiple prompt versions.
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Token Counter
Real-time tokenizer for GPT & Claude.
Cost Tracking
Analytics for model expenditure.
API Endpoints
Deploy prompts as managed endpoints.
Auto-Eval
Quality scoring using similarity benchmarks.