This is a comprehensive collection of ideas that helps dev work with LLMs better in production. For example, RAG (Retrieval-Augmented Generation) is great at improving LLM performance and is preferred over fine-tuning for adding new knowledge to a model's context. There are tips on prompting models better, such as using JSON or XML to structure inputs and outputs. There are also guidelines on evaluating and monitoring LLM I/O properly in areas where LLMs are in a production-level pipeline.