Summary: This article details practical patterns for integrating large language models (LLMs) into systems and products. It covers seven key patterns: evaluations for performance measurement; retrieval-augmented generation to add external knowledge; fine-tuning for task specialization; caching to reduce latency and cost; guardrails to ensure output quality; defensive UX to handle errors; and user feedback collection to improve the system. Each pattern is explained, including its rationale, mechanics, and practical application. The article concludes by mentioning additional machine learning patterns relevant to LLM development.