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 →Tag: Observability
Introduction to Site Reliability Engineering (SRE) in Azure: Achieving Higher Reliability with AKS and Essential Tools
In the fast-paced world of technology, ensuring the reliability of services is paramount for businesses to thrive. Site Reliability Engineering (SRE) has emerged as a discipline that combines software engineering and systems administration to create scalable and highly reliable software systems. In the Azure cloud environment, Azure Kubernetes Service (AKS) plays a pivotal role in […]
Read more →Tips and Tricks – Implement Structured Logging for Observability
Use structured JSON logging for better searchability and analysis in cloud environments.
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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