Tag: LLM monitoring
What You Cannot See Will Break Your LLM App: A Practitioner Guide to Production Observability
Gourav Singla | | AI monitoring, AI observability, AI quality monitoring, automated AI evaluation, Distributed Tracing, finish reason, hallucination monitoring, LLM cost observability, LLM latency, LLM monitoring, LLM observability, LLM tracing, model-graded evaluation, OpenTelemetry GenAI, OTel GenAI, production AI reliability, production LLMs, prompt failures, prompt regression, provider monitoring, quality drift, token budget alerting, token cost monitoringTraditional application observability was built around a simple mental model: Your code runs, metrics come out and when something breaks, the logs tell you why. Large language models (LLMs) break that model ...
From Reactive Monitoring to AI-Driven Operational Intelligence
Sneha Gullapalli | | AI observability, Amazon Q, Apache Iceberg, automated root cause analysis, AWS CloudWatch, AWS operations, CloudWatch Logs, DevOps observability, Distributed Tracing, Five Whys, GenAI observability, incident response, LLM monitoring, log lake, MTTR reduction, operational intelligence, proactive monitoring., RAG latency, S3 Tables, token usage monitoringTraditional monitoring often meant chasing alerts and toggling between dashboards after an issue had already impacted users. AWS CloudWatch — long the backbone of metrics, logs and traces on AWS — is ...
The Death of the Four Golden Signals: Designing Telemetry for Non-Deterministic Infrastructure
Jyostna Seelam | | AI architecture, AI compliance, AI Governance, AI guardrails, AI infrastructure, AI monitoring, AI observability, AI operations, AI operations strategy., AI performance metrics, AI platform monitoring, AI reliability, AI resilience, AI safety, AI SLIs, AI SLOs, AI system reliability, AI telemetry, AI trust, Enterprise AI, generative AI monitoring, guardrail intervention rate, hallucination detection, LLM monitoring, LLM observability, modern SRE, non-deterministic systems, Observability Engineering, OpenTelemetry, platform engineering, production AI, RAG observability, RAG systems, semantic drift, site reliability engineering, SRE, time to first token, token throughput, TTFT, vector database monitoringIn complex software systems, our traditional definition of operational health has always been comfortably binary. For over a decade, site reliability engineering (SRE) teams have relied on the industry-standard ‘Four Golden Signals’ ...
