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lmql-constraints

Automate constrained LLM generation with grammar-like constraints and typed placeholders.

6|1|Updated Nov 29, 2025
Search Tags:#Python#structured output#LLM prompts#lmql#constraint-based generation#typed placeholders#constrained decoding
One-click install
npx skills add https://github.com/manutej/categorical-meta-prompting --skill lmql-constraints
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lmql-constraints
Source: https://github.com/manutej/categorical-meta-prompting/tree/main/.claude/skills/lmql-constraints
Command: npx skills add https://github.com/manutej/categorical-meta-prompting --skill lmql-constraints

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a formal, programmable approach to building constrained, reliable prompts for LLMs, reducing drift, ambiguity, and unpredictable outputs in production prompts.

Core Features & Use Cases

  • Constrained generation: Define output formats and constraints (length, regex, membership) to guarantee structure.
  • Type-safety & composability: Build queries that preserve types and allow safe composition of multiple constrained steps.
  • Real-world use case: Create chat assistants and data extraction pipelines that always produce structured results (e.g., JSON) and predictable behavior.

Quick Start

Install lmql via pip and run a minimal constrained query example. Define a query using the lmql syntax to produce a JSON-like dictionary with fields such as name and age, constrained to valid types, then execute it to verify deterministic output.

Frequently Asked Questions about lmql-constraints

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enforce structured output and constraints in LLM prompts?▼

You can enforce structured output in LLM prompts by using grammar-like constraints, typed placeholders, and constraint patterns like regex and length limits to produce deterministic, type-safe results.

What is constrained decoding for large language models?▼

Constrained decoding is a generation mechanism that applies formal constraints, such as membership and regex patterns, to LLM outputs to guarantee structure and prevent unpredictable text drift.

Do I need the lmql Python library to generate deterministic JSON from an LLM?▼

Yes, you need the lmql Python library and a Python environment to define and execute composable queries that safely extract structured JSON-like dictionaries from LLMs.

Can I use typed placeholders and regex constraints for data extraction pipelines?▼

Yes, you can build data extraction pipelines using typed placeholders and regex constraints to compose multiple safe generation steps, ensuring predictable behavior and valid structured outputs.

What is the best way to prevent unpredictable outputs in production LLM prompts?▼

The best way to prevent unpredictable outputs is adopting a formal, programmable approach with constraint-based generation to reduce ambiguity and enforce deterministic, reliable behavior in production prompts.

What are the limitations of using constraint-based generation for LLMs?▼

Constraint-based generation requires a Python environment and the lmql library, meaning it is limited to contexts where you can execute custom Python code to define and run the constrained queries.

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