prompt-engineering-guidance

原文涵盖的主题

• Guidance: Constrained LLM Generation
• When to Use This Skill
• Installation
• Quick Start
• Basic Example: Structured Generation
• With Anthropic Claude
• Core Concepts
• 1. Context Managers

Use Guidance when you need to: Control LLM output syntax with regex or grammars Guarantee valid JSON/XML/code generation Reduce latency vs traditional prompting approaches Enforce structured formats (dates, emails, IDs, etc.) Build multi-step workflows with Pythonic control flow Prevent invalid outputs through grammatical constraints GitHub Stars: 18,000+ | From: Microsoft Research Guidance uses Pythonic context managers for chat-style interactions. Benefits: Natural chat flow Clear role separation Easy to read and maintain Guidance ensures outputs match specified patterns using regex or grammars. How it works: Regex converted to grammar at token level Invalid tokens filtered during generation Model can only produce matching outputs Guidance automatically "heals" token boundaries between prompt and generation. Problem: Tokenization creates unnatural boundaries. Solution: Guidance backs up one token and regenerates. Benefits: Natural text boundaries No awkward spacing issues Better model performance (sees natural token sequences) Define complex structures using context-free grammars. Use cases: Complex structured outputs Nested data structures Programming language syntax Domain-spec

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