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; […]
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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 →Mastering AWS, EKS, Python, Kubernetes, and Terraform for Monitoring and Observability for SRE: Unveiling the Secrets of Cloud Infrastructure Optimization
As the world of software development continues to evolve, the need for robust infrastructures and efficient monitoring systems cannot be overemphasized. Whether you are an engineer, a site reliability engineer (SRE), or an IT manager, the need to harness the power of tools like Amazon Web Services (AWS), Elastic Kubernetes Service (EKS), Kubernetes, Terraform, and […]
Read more →LLM Evaluation: Metrics, Benchmarks, and A/B Testing
Introduction: Evaluating LLM outputs is challenging because there’s often no single “correct” answer. Traditional metrics like BLEU and ROUGE fall short for open-ended generation. This guide covers modern evaluation approaches: automated metrics for specific tasks, LLM-as-judge for quality assessment, human evaluation frameworks, A/B testing in production, and building comprehensive evaluation pipelines. These techniques help you […]
Read more →LLM Observability: Cost Tracking and Quality Monitoring (Part 2 of 2)
Introduction: You can’t improve what you can’t measure. LLM applications are notoriously difficult to debug—prompts are opaque, responses are non-deterministic, and failures often manifest as subtle quality degradation rather than crashes. Observability gives you visibility into every LLM call: what prompts were sent, what responses came back, how long it took, how much it cost, […]
Read more →LLM Application Logging and Tracing: Building Observable AI Systems
Introduction: Production LLM applications require comprehensive logging and tracing to debug issues, monitor performance, and understand user interactions. Unlike traditional applications, LLM systems have unique logging needs: capturing prompts and responses, tracking token usage, measuring latency across chains, and correlating requests through multi-step workflows. This guide covers practical logging patterns: structured request/response logging, distributed tracing […]
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