Category: Emerging Technologies

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… Continue reading

Introduction: Prompts are the interface between your application and LLMs. As applications grow, managing prompts becomes challenging—they’re scattered across code, hard to version, and difficult to test. A prompt template system brings order to this chaos. It separates prompt logic from application code, enables versioning and A/B testing, and makes prompts reusable across different contexts.… Continue reading

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,… Continue reading

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… Continue reading

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:… Continue reading

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… Continue reading

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… Continue reading

Introduction: Neither keyword search nor semantic search is perfect alone. Keyword search excels at exact matches and specific terms but misses semantic relationships. Semantic search understands meaning but can miss exact phrases and rare terms. Hybrid search combines both approaches, leveraging the strengths of each to deliver superior retrieval quality. This guide covers practical hybrid… Continue reading

Introduction: Tokens are the currency of LLM applications—every token costs money and consumes context window space. Efficient token usage directly impacts both cost and capability. This guide covers practical token optimization techniques: accurate token counting across different models, content compression strategies that preserve meaning, budget management for staying within limits, and prompt engineering patterns that… Continue reading

Introduction: LLM applications are notoriously difficult to debug and monitor. Unlike traditional software where inputs and outputs are deterministic, LLMs produce variable outputs that can fail in subtle ways. Observability—the ability to understand system behavior from external outputs—is essential for production LLM systems. This guide covers practical observability patterns: distributed tracing for complex LLM chains,… Continue reading