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 […]
Read more →Search Results for: events
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 […]
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 […]
Read more →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 […]
Read more →Guardrails and Safety for LLMs: Building Secure AI Applications with Input Validation and Output Filtering
Introduction: Production LLM applications need guardrails to ensure safe, appropriate outputs. Without proper safeguards, models can generate harmful content, leak sensitive information, or produce responses that violate business policies. Guardrails provide defense-in-depth: input validation catches problematic requests before they reach the model, output filtering ensures responses meet safety standards, and content moderation prevents harmful generations. […]
Read more →Rate Limiting for LLM APIs: Token Buckets, Queues, and Adaptive Throttling
Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request limits. Exceeding these limits results in 429 errors that can cascade through your application. Effective rate limiting on your side prevents hitting API limits, provides fair access across users, and enables graceful degradation under load. This guide covers practical rate […]
Read more →