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prompt-engineering

Design structured prompts with roles, chain-of-thought, and XML tags for LLM outputs.

24|Updated Jan 5, 2026
Search Tags:#llm#best-practices#prompt-engineering#prompt#instruction-design#role-prompting#xml-tags
One-click install
npx skills add https://github.com/itsmostafa/llm-engineering-skills --skill prompt-engineering-itsmostafa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/itsmostafa/llm-engineering-skills/tree/main/skills/prompt-engineering
Command: npx skills add https://github.com/itsmostafa/llm-engineering-skills --skill prompt-engineering-itsmostafa

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prompt engineering enables designing inputs that guide LLMs to produce desired outputs, reducing errors and increasing consistency across tasks.

Core Features & Use Cases

  • Structured prompting templates: use steps, roles, and constraints to guide model behavior.
  • Role prompting and chain-of-thought prompts to elicit reasoning or specific styles.
  • XML tag-style prompts and long-context prompts for parsing and structured outputs.
  • Self-verification prompts to validate outputs and catch errors.

Quick Start

Craft a clear, structured prompt that asks the model to summarize a document with bullets and provide citations.

Frequently Asked Questions about prompt-engineering

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

FAQPage Schema
How do I write prompts that reliably guide LLM responses?▼

To guide LLM responses reliably, use structured prompting with explicit context, clear instructions, and role prompting to define model behavior and constraints.

What is chain-of-thought elicitation in prompt engineering?▼

Chain-of-thought elicitation is a prompt engineering technique that structures instructions to guide LLMs through step-by-step reasoning, increasing output accuracy for complex tasks.

When should I use XML tags in my prompts?▼

Use XML tags in prompts when parsing long-context inputs or requiring structured outputs, as they help LLMs distinguish between instructions, context, and data boundaries.

How do I validate LLM outputs and catch errors during generation?▼

You can validate LLM outputs and catch errors by applying self-verification prompt templates, which instruct the model to check its own responses against explicit constraints.

What is the best way to structure prompts for complex workflows?▼

The best way to structure prompts for complex workflows is using structured templates with defined steps, roles, and constraints to enforce explicit context and robust instruction design.

Why does my LLM output lack consistency across different tasks?▼

LLM output lacks consistency when prompts miss explicit context or structured instructions; applying role prompting and self-verification enforces consistent behavior across tasks.

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