Month: August 2024

Introduction: LLM APIs have strict rate limits—requests per minute, tokens per minute, and concurrent request limits. Exceeding these limits results in 429 errors that can cascade through your application. Effective rate limiting on your side prevents hitting API limits, provides fair access across users, and enables graceful degradation under load. This guide covers practical rate […]

Introduction: Vector embeddings are the foundation of modern AI applications—from semantic search to RAG systems to recommendation engines. They transform text, images, and other data into dense numerical representations that capture semantic meaning, enabling machines to understand similarity and relationships in ways that traditional keyword matching never could. This guide provides a deep dive into […]

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: 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 […]