Declarative Llm Prompt Specification With Constraint Based Control Flow

via “constraint-driven text generation with runtime enforcement”

Programming language for constrained LLM interaction.

Unique: Translates character-level constraints to token-level masks during decoding (not post-hoc), enabling eager enforcement and preventing wasted tokens on invalid outputs. Most frameworks (Guidance, Outlines) filter after generation; LMQL integrates constraints into the decoding loop itself.

vs others: More token-efficient than post-hoc filtering frameworks because constraints are enforced during generation, preventing the model from producing invalid tokens in the first place.

via “explicit-control-flow-ownership”

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

Unique: Implements control flow as explicit deterministic code that validates and constrains agent decisions rather than trusting the LLM to follow implicit rules, ensuring business logic and safety constraints are enforced regardless of agent reasoning

vs others: More reliable than prompt-based control flow because it uses code-level enforcement rather than relying on the LLM to follow instructions, preventing agents from bypassing constraints through creative reasoning

via “declarative llm prompt specification with constraint-based control flow”

LMQL is a query language for large language models.

Unique: Uses a compiled query language with runtime constraint enforcement during token generation (not post-processing), enabling early termination and branching based on partial outputs; constraint evaluation is integrated into the generation loop rather than applied after completion

vs others: More expressive and efficient than string-based prompt templates (no post-processing needed) and more declarative than imperative prompt engineering libraries, with constraints enforced at generation time rather than validated afterward

via “constraint-based-output-control”