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, […]
Read more →Category: Emerging Technologies
Emerging technologies include a variety of technologies such as educational technology, information technology, nanotechnology, biotechnology, cognitive science, psychotechnology, robotics, and artificial intelligence.
Function Calling Patterns: Enabling LLMs to Take Real Actions
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 […]
Read more →RAG Query Optimization: Transforming User Questions into Effective Retrieval
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: […]
Read more →LLM Output Validation: Ensuring Reliable Structured Data from Language Models
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 […]
Read more →Multi-Agent Coordination: Building Systems Where AI Agents Collaborate
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 […]
Read more →Hybrid Search Strategies: Combining Keyword and Semantic Search for Superior Retrieval
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 […]
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