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prompt-engineer

Design, optimize, and evaluate LLM prompts across RAG workflows and benchmarks.

1|Updated Apr 23, 2026
Search Tags:#langchain#llm#rag#evaluation#fine-tuning#prompt#llamaindex
One-click install
npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill prompt-engineer-mtsatryan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/mtsatryan/openclaw-ai-agents/tree/main/prompt-engineer
Command: npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill prompt-engineer-mtsatryan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This prompt-engineer skill provides structured guidance and tooling to design, optimize, and evaluate prompts for large language models, improving reliability, safety, and task accuracy across diverse applications.

Core Features & Use Cases

  • Prompt design and optimization techniques for LLMs
  • Retrieval-Augmented Generation (RAG) integration and guidance
  • Fine-tuning, transfer learning, and evaluation benchmarks
  • Chain-of-thought and few-shot prompting strategies
  • LangChain and LlamaIndex framework integration and tooling
  • Model evaluation, benchmarking, and safety alignment
  • Use Case: Build robust prompts for multi-step reasoning tasks, QA agents, and data extraction pipelines.

Quick Start

Generate a prompt design plan for building an RAG-powered QA assistant.

Frequently Asked Questions about prompt-engineer

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

FAQPage Schema
How do I design and optimize prompts for LLM systems to improve task accuracy?▼

To design and optimize LLM prompts, apply structured techniques like chain-of-thought and few-shot prompting to guide multi-step reasoning, QA agents, and data extraction pipelines for higher reliability.

What is the best way to integrate prompt engineering into a RAG workflow?▼

Integrating prompt engineering into a RAG workflow involves using framework-specific tooling from LangChain and LlamaIndex to structure retrieval prompts, maximizing usefulness and accuracy for QA assistants.

Can I use this prompt engineering approach with LangChain and LlamaIndex frameworks?▼

Yes, the prompt engineering approach supports direct integration and tooling for both LangChain and LlamaIndex frameworks, enabling structured prompt design across multiple LLM providers.

How do I evaluate and benchmark LLM prompts for safety and reliability?▼

Evaluate and benchmark LLM prompts by applying structured evaluation benchmarks and safety alignment checks, measuring task accuracy and reliability before deploying prompts to production systems.

When should I use few-shot prompting versus fine-tuning for LLM applications?▼

Use few-shot prompting to guide LLM behavior with examples for quick task adaptation, whereas fine-tuning requires modifying model weights for deeper, consistent behavior changes across complex reasoning tasks.

Does prompt engineering support multi-step reasoning tasks and data extraction pipelines?▼

Yes, prompt engineering supports multi-step reasoning tasks and data extraction pipelines by providing structured prompt design plans that optimize LLM performance and reliability across diverse applications.

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