Author here. LMQL gives you a concise way to define multi-part prompts and enforce constraint on LLMs. For instance, you can make sure the model always adheres

Author here. LMQL gives you a concise way to define multi-part prompts and enforce constraint on LLMs. For instance, you can make sure the model always adheres to a specific output format, where parsing of the output is automatically taken care of. Also abstracts a number of things like APIs and local models, tokenisation, optimisation and makes tool integration (e.g. tool function calls during LLM reasoning) much easier. In practice this saves you a lot of ugly text concatenation and output parsing code, letting you focus on the core logic of your project. Overall, however, you will still use your host language to call LMQL. E.g. we are fully integrated with Python, where LMQL query code simply lives in decorated functions (https://docs.lmql.ai/en/latest/python/python.html https://docs.lmql.ai/en/latest/python/python.html).