Category: Emerging Technologies

Introduction: Context windows are precious real estate. Every token you spend on context is a token you can’t use for output or additional information. Long prompts hit token limits, increase latency, and cost more money. Prompt compression techniques help you fit more information into less space without losing the signal that matters. This guide covers… Continue reading

Introduction: Modern LLMs understand more than text. GPT-4V, Claude 3, and Gemini can process images alongside text, enabling applications that reason across modalities. Building multi-modal applications requires handling image encoding, managing mixed-content prompts, and designing interactions that leverage visual understanding. This guide covers practical patterns for integrating vision capabilities: encoding images for API calls, building… Continue reading

Introduction: How do you know if your LLM application is working well? Subjective assessment doesn’t scale, and traditional NLP metrics often miss what matters for generative AI. Effective evaluation requires multiple approaches: reference-based metrics that compare against gold standards, semantic similarity that measures meaning preservation, and LLM-as-judge techniques that leverage AI to assess AI. This… Continue reading

Introduction: Chatbots and conversational AI need memory. Without conversation history, every message exists in isolation—the model can’t reference what was said before, follow up on previous topics, or maintain coherent multi-turn dialogues. But history management is tricky: context windows are limited, old messages may be irrelevant, and naive approaches quickly hit token limits. This guide… Continue reading

Introduction: LLM API calls are expensive and slow. When users ask similar questions, you’re paying for the same computation repeatedly. Traditional caching doesn’t help because queries are rarely identical—”What’s the weather?” and “Tell me the weather” are different strings but should return the same cached response. Semantic caching solves this by matching queries based on… Continue reading

Introduction: Processing LLM requests one at a time is inefficient. When you have multiple independent requests, sequential processing wastes time waiting for each response before starting the next. Batching groups requests together for parallel processing, dramatically improving throughput. But batching LLM requests isn’t straightforward—you need to handle rate limits, manage concurrent connections, deal with partial… Continue reading

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,… Continue reading

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:… Continue reading

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… Continue reading

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… Continue reading