The Prompt Engineering Patterns skill is a comprehensive toolkit for developers looking to maximize the effectiveness of Large Language Models in production. By implementing sophisticated strategies such as semantic few-shot selection, Pydantic-based structured outputs, and progressive disclosure, this skill provides the necessary scaffolding to build robust, predictable, and high-performing AI applications. It serves as a bridge between basic prompting and production-grade engineering, ensuring that model outputs remain consistent, controllable, and easy to integrate into existing software workflows.
Key Features
01Error recovery and fallback mechanisms for model failures
02Structured JSON output enforcement via Pydantic
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04Dynamic few-shot example selection using semantic similarity
05Chain-of-thought reasoning patterns for complex logic
06Role-based system prompt optimization templates
