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prompt-engineer

Design optimized prompts for LLMs using chain-of-thought and RAG integration.

Updated Aug 23, 2026
Search Tags:#prompt engineering#chain-of-thought#llm optimization#ai system design#constitutional ai#production prompts
One-click install
npx skills add https://github.com/thiagobutignon/nooa-the-pragmatic --skill prompt-engineer-thiagobutignon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/thiagobutignon/nooa-the-pragmatic/tree/main/.agent/skills/prompt-engineer
Command: npx skills add https://github.com/thiagobutignon/nooa-the-pragmatic --skill prompt-engineer-thiagobutignon

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of creating effective, safe, and optimized prompts for Large Language Models (LLMs), ensuring AI systems perform as intended and meet specific business outcomes.

Core Features & Use Cases

  • Advanced Prompting Techniques: Utilizes methods like Chain-of-Thought, Constitutional AI, and meta-prompting for complex reasoning and alignment.
  • Model-Specific Optimization: Tailors prompts for various models (OpenAI, Anthropic, Open Source) considering their unique capabilities and limitations.
  • Production-Ready Systems: Designs prompt templates, integrates with RAG, and orchestrates multi-agent interactions for reliable deployment.
  • Use Case: You need to develop a customer service chatbot that can handle complex queries, provide accurate information, and maintain brand voice. This Skill can help design the core prompts, define its persona, and ensure it aligns with safety guidelines.

Quick Start

Use the prompt-engineer skill to create a chain-of-thought prompt for financial analysis that shows clear reasoning steps.

Frequently Asked Questions about prompt-engineer

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

FAQPage Schema
How do I use chain-of-thought prompting for complex reasoning tasks?▼

Chain-of-thought prompting structures LLM instructions to show clear reasoning steps, enabling complex analysis like financial breakdowns. This approach enhances model performance by guiding sequential logic before final output generation.

What is constitutional AI and how does it improve LLM safety?▼

Constitutional AI aligns LLM outputs with safety guidelines and brand voice by embedding rules directly into the prompt. This ensures customer service chatbots maintain reliability and safe interactions during complex queries.

Can I optimize prompts for both OpenAI and Anthropic models?▼

Model-specific optimization tailors prompts to unique capabilities and limitations of OpenAI, Anthropic, and open source models. This ensures effective prompt crafting by considering specific model architectures.

How do I integrate RAG with production-ready prompt templates?▼

RAG integration combines retrieval mechanisms with production-ready prompt templates to deliver accurate information. This orchestration enables reliable deployment for AI systems handling complex queries.

Does multi-agent design require understanding specific model architectures?▼

Multi-agent design requires understanding specific model architectures and use-case requirements for effective prompt crafting. This ensures proper orchestration of interactions and reliable system deployment.

What is the best way to design prompts for a customer service chatbot?▼

Designing customer service chatbot prompts involves defining personas, ensuring brand voice alignment, and applying safety guidelines. Advanced techniques like meta-prompting handle complex queries while maintaining accuracy.

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