Introduction: Processing documents with LLMs unlocks powerful capabilities: extracting structured data from unstructured text, summarizing lengthy reports, answering questions about document content, and transforming documents between formats. However, effective document processing requires more than just sending text to an LLM—it demands careful parsing, intelligent chunking, and strategic prompting. This guide covers practical document processing patterns: […]
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LLM Observability: Tracing, Metrics, and Logging for Production AI (Part 1 of 2)
Introduction: Observability is essential for production LLM applications—you need visibility into latency, token usage, costs, error rates, and output quality. Unlike traditional applications where you can rely on status codes and response times, LLM applications require tracking prompt versions, model behavior, and semantic quality metrics. This guide covers practical observability: distributed tracing for multi-step LLM […]
Read more →LLM Evaluation Metrics: Automated Testing, LLM-as-Judge, and Human Assessment for Production AI
Introduction: Evaluating LLM outputs is fundamentally different from traditional ML evaluation. There’s no single ground truth for creative tasks, quality is subjective, and outputs vary with each generation. Yet rigorous evaluation is essential for production systems—you need to know if your prompts are working, if model changes improve quality, and if your system meets user […]
Read more →Building AI Agents with Tool Use: From ReAct to Production Systems
Introduction: AI agents represent the next evolution beyond simple chatbots—they can reason about problems, break them into steps, use external tools, and iterate until they achieve a goal. Unlike traditional LLM applications that respond to a single prompt, agents maintain state, make decisions, and take actions in the real world. The key innovation is tool […]
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