prompt-engineering-outlines

Topics in the source

• Outlines: Structured Text Generation
• When to Use This Skill
• Installation
• Quick Start
• Basic Example: Classification
• With Pydantic Models
• Core Concepts
• 1. Constrained Token Sampling

Use Outlines when you need to: Guarantee valid JSON/XML/code structure during generation Use Pydantic models for type-safe outputs Support local models (Transformers, llama.cpp, vLLM) Maximize inference speed with zero-overhead structured generation Generate against JSON schemas automatically Control token sampling at the grammar level GitHub Stars: 8,000+ | From: dottxt.ai (formerly .txt) Outlines uses Finite State Machines (FSM) to constrain token generation at the logit level. How it works: 1. Convert schema (JSON/Pydantic/regex) to context-free grammar (CFG) 2. Transform CFG into Finite State Machine (FSM) 3. Filter invalid tokens at each step during generation 4. Fast-forward when only one valid token exists Benefits: Zero overhead: Filtering happens at token level Speed improvement: Fast-forward through deterministic paths Guaranteed validity: Invalid outputs impossible Outlines provides specialized generators for different output types. Outlines supports multiple local and API-based backends. Outlines has first-class Pydantic support with automatic schema translation. | Feature | Outlines | Instructor | Guidance | LMQL | |---------|----------|------------|----------|------|

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