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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: 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 […]

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, […]

Introduction: LLMs generate text, but applications need structured data—JSON objects, database records, API payloads. Getting reliable structured output from language models requires more than asking nicely in the prompt. This guide covers practical techniques for structured generation: defining schemas with Pydantic or JSON Schema, using constrained decoding to guarantee valid output, implementing retry logic with […]