Category: Emerging Technologies

Introduction: Context windows are the most valuable resource in LLM applications. Every token matters—waste space on irrelevant content and you lose room for information that could improve responses. Effective context window optimization means fitting the right information in the right amount of space. This guide covers practical strategies: prioritizing content by relevance, chunking documents intelligently, […]

Introduction: Complex tasks often exceed what a single LLM call can handle well. Breaking problems into smaller steps—where each step’s output feeds into the next—produces better results than trying to do everything at once. Prompt chaining decomposes complex workflows into sequential LLM calls, each focused on a specific subtask. This guide covers practical chaining patterns: […]

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 […]

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 […]

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 […]

Introduction: Choosing the right embedding model determines the quality of your semantic search, RAG system, or clustering application. Different models excel at different tasks—some optimize for retrieval accuracy, others for speed, and others for specific domains. The wrong choice means poor results regardless of how well you build everything else. This guide covers practical embedding […]