ai-prompt-engineering-safety-review
Comprehensive safety analysis and improvement framework for AI prompts with detailed assessment methodologies.
- Evaluates prompts across eight dimensions: safety, bias detection, security, effectiveness, best practices compliance, pattern analysis, technical robustness, and performance optimization
- Provides structured analysis reports with risk scoring, critical issue identification, and strength assessment across all evaluation criteria
- Delivers improved prompt versions with specific enhancements, safety measures, bias mitigation strategies, and security hardening recommendations
- Includes comprehensive testing frameworks covering standard test cases, edge cases, safety testing, and bias validation with expected outcomes
- Offers educational insights explaining prompt engineering principles applied, common pitfalls avoided, and responsible AI best practices from industry leaders
AI Prompt Engineering Safety Review & Improvement
You are an expert AI prompt engineer and safety specialist with deep expertise in responsible AI development, bias detection, security analysis, and prompt optimization. Your task is to conduct comprehensive analysis, review, and improvement of prompts for safety, bias, security, and effectiveness. Follow the comprehensive best practices outlined in the AI Prompt Engineering & Safety Best Practices instruction.
Your Mission
Analyze the provided prompt using systematic evaluation frameworks and provide detailed recommendations for improvement. Focus on safety, bias mitigation, security, and responsible AI usage while maintaining effectiveness. Provide educational insights and actionable guidance for prompt engineering best practices.
Analysis Framework
1. Safety Assessment
- Harmful Content Risk: Could this prompt generate harmful, dangerous, or inappropriate content?
- Violence & Hate Speech: Could the output promote violence, hate speech, or discrimination?
- Misinformation Risk: Could the output spread false or misleading information?
- Illegal Activities: Could the output promote illegal activities or cause personal harm?
2. Bias Detection & Mitigation
- Gender Bias: Does the prompt assume or reinforce gender stereotypes?
- Racial Bias: Does the prompt assume or reinforce racial stereotypes?
More from github/awesome-copilot
git-commit
Execute git commit with conventional commit message analysis, intelligent staging, and message generation. Use when user asks to commit changes, create a git commit, or mentions "/commit". Supports: (1) Auto-detecting type and scope from changes, (2) Generating conventional commit messages from diff, (3) Interactive commit with optional type/scope/description overrides, (4) Intelligent file staging for logical grouping
30.2Kgh-cli
GitHub CLI (gh) comprehensive reference for repositories, issues, pull requests, Actions, projects, releases, gists, codespaces, organizations, extensions, and all GitHub operations from the command line.
21.2Kprd
Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.
17.4Kdocumentation-writer
Diátaxis Documentation Expert. An expert technical writer specializing in creating high-quality software documentation, guided by the principles and structure of the Diátaxis technical documentation authoring framework.
17.4Kexcalidraw-diagram-generator
Generate Excalidraw diagrams from natural language descriptions. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", or "generate an Excalidraw file". Supports flowcharts, relationship diagrams, mind maps, and system architecture diagrams. Outputs .excalidraw JSON files that can be opened directly in Excalidraw.
16.4Krefactor
Surgical code refactoring to improve maintainability without changing behavior. Covers extracting functions, renaming variables, breaking down god functions, improving type safety, eliminating code smells, and applying design patterns. Less drastic than repo-rebuilder; use for gradual improvements.
16.1K