Observability Practices in AI Engineering: A Complete Guide to LLM Monitoring

Master AI observability with this comprehensive guide. Compare Langfuse, Helicone, LangSmith, and other tools. Learn which metrics matter, how to build evaluation pipelines, and implement production-grade monitoring for LLM applications.

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Cost Optimization for AI Workloads: Tracking and Reducing LLM Costs

Last quarter, our LLM costs hit $12,000. In a single month. We had no idea where the money was going. No tracking, no budgets, no alerts. That’s when I realized: cost optimization isn’t optional for AI workloads—it’s survival. Here’s how we cut costs by 65% without sacrificing quality. Figure 1: Cost Optimization Architecture The $12,000 […]

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Prompt Performance Monitoring: Tracking LLM Response Quality

Three weeks after launching our AI customer support system, we noticed something strange. Response quality was degrading—slowly, almost imperceptibly. Users weren’t complaining yet, but satisfaction scores were dropping. The problem? We had no way to measure prompt performance. We were optimizing blind. That’s when I built a comprehensive prompt performance monitoring system. Figure 1: Prompt […]

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