LMQL introduces a groundbreaking query language meticulously tailored for harnessing the immense potential of Large Language Models (LLMs). This innovative language seamlessly marries the fluency of natural language prompts with the robust expressiveness of Python, thus revolutionizing the interaction paradigm with LLMs. At its core, LMQL serves as a versatile toolbox, equipped with a plethora of invaluable features designed to elevate the art of prompting LLMs. These include constraints, debugging utilities, retrieval capabilities, precise control flow mechanisms, and steadfast support for the 🤗 Transformers framework. This arsenal of tools collectively simplifies the task of extracting nuanced responses from LLMs, ushering in a new era of interaction. LMQL further streamlines the process with an extensive catalog of meticulously crafted pre-built prompts, spanning diverse tasks like humor generation, crafting packing lists, probing Wikipedia’s depths, or engaging in dynamic conversations with AI bots. These prompts serve as a robust foundation for various applications. Notably, LMQL empowers users with the ability to exert granular control over the generation process. It supports regular Python control flow statements, allowing for the programmatic orchestration of prompts, and automatically manages token generation. Critically, LMQL rigorously validates generated sequences, ensuring the utmost precision in responses. Going a step beyond, LMQL boasts seamless integration with arbitrary Python code within the prompt clause, facilitating dynamic prompts and sophisticated text processing. The Scripted Beam Search feature operates in tandem with users, exploring multiple avenues to decipher expert names and answers. Additionally, LMQL leverages Python’s assert capability to validate generated outputs, a valuable asset for dataset evaluation. In summation, LMQL emerges as a formidable force, streamlining the intricate dance between users and LLMs. It empowers Python developers with a potent tool to interact with natural language prompts efficiently, ushering in a new frontier of possibilities in the realm of AI-driven language models.