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LMQL is a programming language tailored for large language models (LLMs). It offers robust and modular LLM prompting through the use of types, templates, constraints, and an optimizing runtime. It simplifies the creation of complex prompts by allowing procedural programming techniques in a query-like syntax. Created by the SRI Lab at ETH Zurich, LMQL supports features such as nested queries, scripted prompting, and custom constraints. It also provides a Playground IDE for ease of use.
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Nested Queries
Scripted Prompting
Custom Constraints
Optimizing Runtime
Playground IDE
Local Model Support
Tool Augmentation
High-level Constraint Management
Sequential Query Execution
Integration with Popular Libraries
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Creating complex and modular prompt structures for LLMs.
Implementing advanced prompting techniques and optimization strategies.
Utilizing structured prompting for data analysis and interpretation.
Building intelligent chatbots and interactive systems.
Teaching and demonstrating advanced prompting and LLM capabilities.
Experimenting with LLMs and custom prompt designs.
Optimizing internal and customer-facing interactions through advanced LLM prompting.
Exploring the potential of procedural programming in LLM prompting.
Innovating new applications and solutions using optimized LLM prompts.
Participating in the development and documentation of LMQL features.
Get the latest AI workflows to boost your productivity and business performance, delivered weekly by expert consultants. Enjoy step-by-step guides, weekly Q&A sessions, and full access to our AI workflow archive.