Category: Technology Engineering

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, […]

Introduction: Prompts are code—they define your application’s behavior and should be managed with the same rigor as source code. Yet many teams treat prompts as ad-hoc strings scattered throughout their codebase, making it impossible to track changes, compare versions, or systematically improve performance. This guide covers practical prompt management: version control systems for prompts, A/B […]

Introduction: Vector search finds semantically similar content, but it misses the structured relationships that make knowledge truly useful. Knowledge graphs capture entities and their relationships explicitly—who works where, what depends on what, how concepts connect. Combining knowledge graphs with LLMs creates systems that can reason over structured relationships while generating natural language responses. This guide […]

Introduction: Fine-tuning transforms general-purpose language models into specialized tools for your domain. While prompting works for many tasks, fine-tuning delivers consistent behavior, lower latency, and reduced token costs when you need the model to reliably follow specific formats, use domain terminology, or exhibit particular reasoning patterns. This guide covers practical fine-tuning strategies: preparing high-quality training […]