Introduction: LLM APIs fail. Rate limits get hit, servers time out, responses get truncated, and models occasionally return garbage. Production applications need robust error handling that gracefully recovers from failures without losing user context or corrupting state. This guide covers practical error handling strategies: detecting and classifying different error types, implementing retry logic with exponential […]
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Streaming Response Patterns: Building Responsive LLM Applications
Introduction: Waiting for complete LLM responses creates poor user experiences. Users stare at loading spinners while models generate hundreds of tokens. Streaming delivers tokens as they’re generated, showing users immediate progress and reducing perceived latency dramatically. But streaming introduces complexity: you need to handle partial responses, buffer tokens for processing, manage connection failures mid-stream, and […]
Read more →LLM Monitoring and Observability: Metrics, Traces, and Alerts
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; […]
Read more →LLM Security Best Practices: Protecting AI Applications from Attacks
Introduction: LLM applications face unique security challenges. Prompt injection attacks can hijack model behavior, sensitive data can leak through responses, and malicious outputs can harm users. Traditional security measures don’t fully address these risks—you need LLM-specific defenses. This guide covers practical security strategies: validating and sanitizing inputs, detecting prompt injection attempts, filtering sensitive information from […]
Read more →Streaming Responses for LLMs: Implementing Server-Sent Events
Streaming LLM responses dramatically improves user experience. After implementing streaming for 20+ LLM applications, I’ve learned what works. Here’s the complete guide to implementing Server-Sent Events for LLM streaming. Figure 1: Streaming Architecture Why Streaming Matters Streaming LLM responses provides significant benefits: Perceived performance: Users see results immediately, not after 10+ seconds Better UX: Progressive […]
Read more →What Is Retrieval-Augmented Generation (RAG)?
Introduction Welcome to a fascinating journey into the world of AI innovation! Today, we delve into the realm of Retrieval-Augmented Generation (RAG) – a cutting-edge technique revolutionizing the way AI systems interact with external knowledge. Imagine a world where artificial intelligence not only generates text but also taps into vast repositories of information to deliver […]
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