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12 articles
Build a comprehensive analytics backend for AI features. Track queries, user satisfaction, funnel conversion, and detect anomalies in AI system behavior.
Implement cost attribution, anomaly detection, and forecasting to prevent runaway LLM spending and optimize your AI infrastructure.
Set up Sentry for error tracking with source map upload, release tracking, performance monitoring, and alert routing that won''t create alert fatigue.
Unify logs, metrics, traces, and profiles in Grafana. Learn Prometheus recording rules, Loki LogQL, Tempo distributed tracing, and correlate signals for faster incident resolution.
Master end-to-end LLM observability with OpenTelemetry spans, Langfuse tracing, and token-level cost tracking to catch production issues before users do.
Implement comprehensive LLM observability with LangSmith/LangFuse integration, token tracking, latency monitoring, cost attribution, quality scoring, and degradation alerts.
Something is wrong in production. Response times spiked. Users are complaining. You SSH into a server and grep logs. You have no metrics, no traces, no dashboards. You''re debugging a distributed system with no instruments — and you will be for hours.
Implement the three pillars: Prometheus metrics, Loki structured logging, and Tempo distributed tracing. Correlate with trace IDs for complete request visibility.
Trace LLM inference with OpenTelemetry semantic conventions. Monitor token counts, latency, agent loops, and RAG pipeline steps with structured observability.
Identify slow queries with pg_stat_statements, read EXPLAIN ANALYZE output, tune work_mem and autovacuum, and configure PgBouncer for connection pooling.
Build comprehensive monitoring for RAG systems tracking retrieval quality, generation speed, user feedback, and cost metrics to detect quality drift in production.
Define meaningful SLOs and SLIs that align product and engineering. Implement error budgets to enable fast iteration without breaking production.