Category: Technology Engineering

Introduction: Production LLM applications often benefit from using multiple models—routing simple queries to cheaper models, using specialized models for specific tasks, and falling back to alternatives when primary models fail. Multi-model orchestration enables cost optimization, improved reliability, and access to each model’s unique strengths. This guide covers practical orchestration patterns: model routing based on query […]

Introduction: Chatbots without memory feel robotic—they forget your name, repeat questions, and lose context mid-conversation. Production chatbots need sophisticated memory systems: short-term memory for the current conversation, long-term memory for user preferences and history, and summary memory to compress long interactions. This guide covers implementing these memory patterns: conversation buffers, vector-based retrieval, automatic summarization, and […]

Introduction: The era of text-only LLMs is ending. Modern vision-language models like GPT-4V, Claude 3, and Gemini can see images, understand diagrams, read documents, and reason about visual content alongside text. This opens entirely new application categories: document understanding, visual Q&A, image-based search, accessibility tools, and creative applications. This guide covers building multi-modal AI applications […]

Introduction: Long contexts contain valuable information, but they also contain noise, redundancy, and irrelevant details that consume tokens and dilute model attention. Context distillation extracts the essential information from lengthy documents, conversations, or retrieved passages, producing compact representations that preserve what matters while discarding what doesn’t. This technique is crucial for RAG systems processing multiple […]

Introduction: Not all queries are equal—some need fast, cheap responses while others require deep reasoning. Query routing intelligently directs requests to the right model, index, or processing pipeline based on query characteristics. Route simple factual questions to smaller models, complex reasoning to GPT-4, and domain-specific queries to specialized indexes. This approach optimizes both cost and […]

Introduction: Knowledge distillation transfers the capabilities of large, expensive models into smaller, faster ones that can run efficiently in production. Instead of training a small model from scratch, distillation leverages the “dark knowledge” encoded in a teacher model’s soft probability distributions—information that hard labels alone cannot capture. This guide covers the techniques that make distillation […]