Introduction: Retrieval evaluation is the foundation of building effective RAG systems and search applications. Without proper metrics, you’re flying blind—unable to tell if your retrieval improvements actually help or hurt end-user experience. This guide covers the essential metrics for evaluating retrieval systems: precision and recall at various cutoffs, Mean Reciprocal Rank (MRR), Normalized Discounted Cumulative […]
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LLM Application Monitoring: Metrics, Tracing, and Alerting for Production AI Systems
Introduction: LLM applications fail in ways traditional software doesn’t. A model might return syntactically correct but factually wrong responses. Latency can spike unpredictably. Costs can explode without warning. Token usage varies wildly based on input. Traditional APM tools miss these LLM-specific failure modes. This guide covers comprehensive monitoring for LLM applications: tracking latency, tokens, and […]
Read more →LLM Monitoring and Observability: Metrics, Traces, and Alerts
Introduction: LLM applications are notoriously difficult to debug. Unlike traditional software where errors are obvious, LLM issues manifest as subtle quality degradation, unexpected costs, or slow responses. Proper observability is essential for production LLM systems. This guide covers monitoring strategies: tracking latency, tokens, and costs; implementing distributed tracing for complex chains; structured logging for debugging; […]
Read more →Azure Monitor: A Solutions Architect’s Guide to Enterprise Observability
Azure Monitor Architecture – Data Sources, Platform, Insights, and Actions Observability has become the cornerstone of successful cloud operations, and after two decades of building and maintaining enterprise systems, I can confidently say that Azure Monitor represents one of the most comprehensive observability platforms available today. The ability to collect, analyze, and act on telemetry […]
Read more →LLM Monitoring and Alerting: Building Observability for Production AI Systems
Introduction: LLM monitoring is essential for maintaining reliable, cost-effective AI applications in production. Unlike traditional software where errors are obvious, LLM failures can be subtle—degraded output quality, increased hallucinations, or slowly rising costs that go unnoticed until the monthly bill arrives. Effective monitoring tracks latency, token usage, error rates, output quality, and cost metrics in […]
Read more →LLM Chain Debugging: Tracing, Inspecting, and Fixing Multi-Step AI Workflows
Introduction: Debugging LLM chains is fundamentally different from debugging traditional software. When a chain fails, the problem could be in the prompt, the model’s interpretation, the output parsing, or any of the intermediate steps. The non-deterministic nature of LLMs means the same input can produce different outputs, making reproduction difficult. Effective chain debugging requires comprehensive […]
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