Category: Emerging Technologies

Introduction: Processing thousands or millions of items through LLMs requires different patterns than single-request applications. Naive sequential processing is too slow, while uncontrolled parallelism hits rate limits and wastes money on retries. This guide covers production batch processing patterns: chunking strategies, parallel execution with rate limiting, progress tracking, checkpoint/resume for long jobs, cost estimation, and […]

Introduction: Fine-tuning adapts pre-trained language models to specific tasks, domains, or behaviors. While prompting works for many use cases, fine-tuning delivers better performance, lower latency, and reduced costs for specialized applications. This guide covers modern fine-tuning approaches: full fine-tuning for maximum customization, LoRA and QLoRA for efficient parameter updates, preparing high-quality training data, using OpenAI […]

Introduction: LLM outputs are inherently unstructured text, but applications need structured data—JSON objects, typed responses, specific formats. Getting reliable structured output requires careful prompt engineering, output parsing, validation, and error recovery. This guide covers practical output formatting techniques: JSON mode and structured outputs, Pydantic-based parsing, format enforcement with retries, template-based formatting, and strategies for handling […]

Introduction: LLM agents extend language models beyond text generation into autonomous action. By connecting LLMs to tools—web search, code execution, APIs, databases—agents can gather information, perform calculations, and interact with external systems. This guide covers building tool-using agents from scratch: defining tools with schemas, implementing the reasoning loop, handling tool execution, managing conversation state, and […]