Author: Nithin Mohan TK

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 […]

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: 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 […]

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, […]