.NET AI Performance Optimization: Reducing Latency and Costs

Last year, I inherited a .NET AI application that was struggling. Response times averaged 2.3 seconds, costs were spiraling, and users were complaining. After three months of optimization, we cut latency by 87% and reduced costs by 72%. Here’s what I learned about optimizing .NET AI applications for production. Figure 1: .NET AI Performance Optimization […]

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Streaming LLM Responses: SSE, WebSockets, and Real-Time Token Delivery (Part 1 of 2)

Introduction: Streaming responses dramatically improve perceived latency in LLM applications. Instead of waiting seconds for a complete response, users see tokens appear in real-time, creating a more engaging experience. Implementing streaming correctly requires understanding Server-Sent Events (SSE), handling partial tokens, managing connection lifecycle, and gracefully handling errors mid-stream. This guide covers practical streaming patterns: basic […]

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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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Architecting the Moment: Real-Time Data Processing in Modern Cloud Systems

After two decades of architecting data systems across financial services, healthcare, and e-commerce, I’ve witnessed the evolution from batch-only processing to today’s sophisticated real-time architectures. The shift isn’t just about speed—it’s about fundamentally changing how organizations make decisions and respond to events. This article shares battle-tested insights on building production-grade real-time data processing systems in […]

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