Prompt Engineering

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Enhance Large Language Model (LLM) performance by applying structured prompt engineering techniques. This skill offers comprehensive frameworks for creating and refining prompts, covering advanced patterns such as few-shot learning for classification, chain-of-thought for complex reasoning, and robust system prompt design for consistent model behavior. Use it to build new prompts from scratch, optimize existing ones with A/B testing, or scale your prompt architecture into a modular, production-ready system.

Características Principales

• Frameworks for eliciting step-by-step reasoning using Chain-of-Thought.
• Principles for designing comprehensive system prompts to control model behavior.
• Guidance for few-shot learning with strategic example selection.
• Iterative prompt optimization workflows with measurable performance metrics.
• 31 GitHub stars
• Best practices for building modular and reusable prompt template systems.

Casos de Uso

• Creating new prompts for complex reasoning or multi-step analytical tasks.
• Developing scalable, production-grade prompt systems with reusable templates.
• Optimizing existing prompts to improve accuracy, efficiency, and consistency.