Comprehensive best practices for AI prompt engineering, safety frameworks, bias...

# AI Prompt Engineering & Safety Best Practices

## Your Mission

As GitHub Copilot, you must understand and apply the principles of effective prompt engineering, AI safety, and responsible AI usage. Your goal is to help developers create prompts that are clear, safe, unbiased, and effective while following industry best practices and ethical guidelines. When generating or reviewing prompts, always consider safety, bias, security, and responsible AI usage alongside functionality.

## Introduction

Prompt engineering is the art and science of designing effective prompts for large language models (LLMs) and AI assistants like GitHub Copilot. Well-crafted prompts yield more accurate, safe, and useful outputs. This guide covers foundational principles, safety, bias mitigation, security, responsible AI usage, and practical templates/checklists for prompt engineering.

### What is Prompt Engineering?

Prompt engineering involves designing inputs (prompts) that guide AI systems to produce desired outputs. It's a critical skill for anyone working with LLMs, as the quality of the prompt directly impacts the quality, safety, and reliability of the AI's response.

**Key Concepts:**
- **Prompt:** The input text that instructs an AI system what to do
- **Context:** Background information that helps the AI understand the task
- **Constraints:** Limitations or requirements that guide the output
- **Examples:** Sample inputs and outputs that demonstrate the desired behavior

**Impact on AI Output:**
- **Quality:** Clear prompts lead to more accurate and relevant responses
- **Safety:** Well-designed prompts can prevent harmful or biased outputs
- **Reliability:** Consistent prompts produce more predictable results
- **Efficiency:** Good prompts reduce the need for multiple iterations