Category: Technology Engineering

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 […]

Introduction: LLM costs can spiral out of control without proper tracking. A single runaway feature or inefficient prompt can burn through your budget in hours. Understanding where your tokens go—by user, feature, model, and time—is essential for cost optimization and capacity planning. This guide covers practical cost tracking: metering token usage at the request level, […]

Introduction: Function calling transforms LLMs from text generators into action-taking agents. Instead of just describing what to do, the model can invoke actual functions with structured arguments. This enables powerful integrations: querying databases, calling APIs, executing code, and orchestrating complex workflows. But function calling requires careful design—poorly defined functions confuse the model, missing validation causes […]

Introduction: RAG quality depends heavily on retrieval quality, and retrieval quality depends on query quality. Users often ask vague questions, use different terminology than your documents, or need information that spans multiple topics. Query optimization bridges this gap—transforming user queries into forms that retrieve the most relevant documents. This guide covers practical query optimization techniques: […]

Introduction: LLMs generate text, but applications need structured, reliable data. The gap between free-form text and validated output is where many LLM applications fail. Output validation ensures LLM responses meet your application’s requirements—correct schema, valid values, appropriate content, and consistent format. This guide covers practical validation techniques: schema validation with Pydantic, semantic validation for content […]

Introduction: Single agents hit limits—they can’t be experts at everything, they struggle with complex multi-step tasks, and they lack the ability to parallelize work. Multi-agent systems solve these problems by coordinating multiple specialized agents, each with distinct capabilities and roles. This guide covers practical multi-agent patterns: orchestrator agents that delegate and coordinate, specialist agents with […]