prompt-engineering

Name: prompt-engineering
Availability: InStock
Author: Veda

Design and improve prompts for large language models with validation checks.

Updated Feb 17, 2026

Search Tags:#prompt engineering #validation #llm #prompt design #few-shot #agent workflows #instruction hierarchy

Ready to run

npx skills add https://github.com/VedaAstro/veda-skills --skill prompt-engineering-vedaastro

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/VedaAstro/veda-skills/tree/main/prompt-engineering
Command: npx skills add https://github.com/VedaAstro/veda-skills --skill prompt-engineering-vedaastro

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you create, refine, and debug prompts for language models when the output is vague, inconsistent, off-format, or simply not doing what you intended.

Core Features & Use Cases

Prompt Design: Turn a rough task into a structured prompt with clear intent, constraints, examples, and output rules.
Prompt Debugging: Diagnose why an LLM is missing the point, ignoring instructions, or producing unstable formats.
Production Prompting: Build stronger prompts for agents, RAG workflows, and long-lived assistant behavior with validation and instruction hierarchy.
Use Case: You can use this Skill to rewrite a weak customer-support prompt into a robust version that produces concise, safe, and consistently formatted replies.

Quick Start

Ask the prompt-engineering skill to analyze your prompt, identify what is failing, and rewrite it into a clearer version with stronger instructions and validation.

Frequently Asked Questions about prompt-engineering

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

FAQPage Schema

Why does my LLM prompt keep producing vague or inconsistent outputs?▼

Vague or inconsistent LLM outputs usually stem from missing explicit intent framing, weak constraints, or conflicting instructions. Diagnosing the prompt structure and applying an instruction hierarchy with validation checks produces stable, reliable results.

How do I design a prompt for agent workflows that won't break in production?▼

To design robust agent workflow prompts, structure the task with clear intent, add few-shot examples, enforce output rules, and implement validation checks. Applying an instruction hierarchy prevents the agent from drifting or ignoring critical system instructions over time.

What is instruction hierarchy in prompt engineering?▼

Instruction hierarchy in prompt engineering is the structured prioritization of system, user, and skill prompts. It safeguards against instruction conflicts and formatting breaks, ensuring the LLM consistently follows the highest-priority constraints and outputs reliable results.

How do I debug a prompt that is ignoring my formatting instructions?▼

Debug a prompt ignoring formatting instructions by analyzing the prompt for conflicting commands and weak output rules. Rewriting the prompt with explicit intent framing, strict constraints, and validation checks enforces the desired formatting and resolves the drift.

Can I use few-shot examples to fix unstable LLM outputs?▼

Yes, you can use few-shot examples to fix unstable LLM outputs. Providing clear examples within the prompt design constrains the model's behavior, clarifies the expected format, and works alongside validation checks to produce consistently formatted replies.

When should I rewrite a weak system prompt for my RAG workflow?▼

You should rewrite a weak system prompt for your RAG workflow when outputs drift, instructions conflict, or formatting breaks. Upgrading the prompt with explicit intent framing and an instruction hierarchy ensures long-lived, reliable assistant behavior in production.