Guidance enables you to control modern language models more effectively and efficiently than traditional prompting or chaining. Guidance programs allow you to interleave generation, prompting, and logical control into a single continuous flow matching how the language model actually processes the text. Simple output structures like Chain of Thought and its many variants (e.g., ART, Auto-CoT, etc.) have been shown to improve LLM performance. The advent of more powerful LLMs like GPT-4 allows for even richer structure, and guidance makes that structure easier and cheaper.