how-to-write-excellent-prompts

Define explicit goals, constraints, and stepwise workflows for LLM prompts.

7|2|Updated Aug 24, 2023

Search Tags:#workflow #templates #llm #constraints #prompt-engineering #prompt-writing

Ready to run

npx skills add https://github.com/pingdior/usingSkills --skill how-to-write-excellent-prompts

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: how-to-write-excellent-prompts
Source: https://github.com/pingdior/usingSkills/tree/main/how-to-write-excellent-prompts
Command: npx skills add https://github.com/pingdior/usingSkills --skill how-to-write-excellent-prompts

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses unclear or under-specified prompts that lead to low-quality or unpredictable LLM outputs by teaching how to define explicit goals, constraints, and executable workflows.

Core Features & Use Cases

Two core principles: everything describable in characters is solvable by LLMs, and clarity of goal and constraints is paramount.
Practical guidance: how to translate user intent into explicit goals, specify constraints, and design stepwise workflows for reliable outputs.
Use cases: crafting data-extraction prompts, training prompt-engineering best practices, and converting vague requests into structured tasks for assistants.

Quick Start

Extract all email addresses from the provided text, output one validated address per line, and include a brief note about the validation rules.

Frequently Asked Questions about how-to-write-excellent-prompts

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

FAQPage Schema

How do I write clear prompts for large language models?▼

To write clear prompts for large language models, define explicit goals, specify strict constraints, and design stepwise workflows to translate user intent into reliable text generation outputs.

Why does my LLM output unpredictable results from vague requests?▼

LLM outputs are unpredictable from vague requests due to under-specified goals and missing constraints. Structuring vague requests into explicit, constraint-driven workflows increases model understanding and output reliability.

What is the best way to structure prompt engineering workflows for data extraction?▼

The best way to structure prompt engineering workflows for data extraction is to specify explicit validation rules, define the exact output format, and provide stepwise templates for the model to follow.

Can I use constraint-driven prompts to improve text generation reliability?▼

Yes, you can use constraint-driven prompts to improve text generation reliability by defining explicit goals and applying stepwise workflow templates that guide the model through structured processing steps.

When should I use stepwise workflow templates in prompt writing?▼

You should use stepwise workflow templates in prompt writing when translating complex user intent into structured tasks, ensuring the model executes data extraction and validation in predictable stages.

How to convert vague requests into structured tasks for prompt engineering?▼

Convert vague requests into structured tasks by setting clear goals, specifying explicit constraints, and designing stepwise workflows that enforce reliable text generation and data extraction rules.