Category: Emerging Technologies

Introduction: LLM applications face unique security challenges. Prompt injection attacks can hijack model behavior, sensitive data can leak through responses, and malicious outputs can harm users. Traditional security measures don’t fully address these risks—you need LLM-specific defenses. This guide covers practical security strategies: validating and sanitizing inputs, detecting prompt injection attempts, filtering sensitive information from… Continue reading

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