Introduction: LLM API calls are inherently I/O-bound—waiting for network responses dominates execution time. Async programming transforms this bottleneck into an opportunity for massive parallelism. Instead of waiting sequentially for each response, async patterns enable concurrent execution of hundreds of requests while efficiently managing resources. This guide covers practical async patterns for LLM applications: concurrent request […]
Read more →Category: Technology Engineering
Technology Engineering
Prompt Template Management: Engineering Discipline for LLM Prompts
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. […]
Read more →Document Processing with LLMs: Enterprise Parsing, Chunking, and Extraction (Part 2 of 2)
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: […]
Read more →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 →Text-to-SQL with LLMs: Building Natural Language Database Interfaces
Introduction: Natural language to SQL is one of the most practical LLM applications. Business users can query databases without knowing SQL, analysts can explore data faster, and developers can prototype queries quickly. But naive implementations fail spectacularly—generating invalid SQL, hallucinating table names, or producing queries that return wrong results. This guide covers building robust text-to-SQL […]
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