P.R.O.M.P.T.
A Principle-Based Approach for LLM-Human Communication
Transform prompt engineering from ad-hoc guessing to systematic methodology. Any pattern can (and should) be broken down to principles.
The Six Principles
Click on any component to explore its purpose, examples, and best practices.
Click on any letter above to explore that component in detail
Interactive Prompt Builder
Build your prompt step-by-step using all six components. Watch it come together in real-time.
Assembled Prompt
Start filling in the components on the left to build your prompt...
Try It Live
Transform any prompt using the P.R.O.M.P.T framework — paste your prompt, get back a structured version.
Launch P.R.O.M.P.T BotPowered by GPT-4 • Free to use
Real-World Examples
See P.R.O.M.P.T. in action across different domains.
Security Assessment
CybersecurityView assembled prompt ▾
You are a senior security analyst preparing a Q1 vulnerability assessment for the CISO and security team (familiar with NIST CSF).
Create a vulnerability assessment report with:
- Executive summary (2-3 paragraphs)
- Markdown table: Vulnerability | CVSS 3.1 Score | CVE ID | MITRE ATT&CK Mapping | Status
- Prioritized remediation steps
Guidelines:
- Only include confirmed findings from scan results (no theoretical vulnerabilities)
- Professional tone throughout
- Technical details in appendix section
- Use CVSS 3.1 scoring methodology
Technical Blog Post
Content CreationView assembled prompt ▾
Write a technical blog post about prompt injection attacks for web developers who are new to AI security but experienced in web development.
Structure:
- Engaging introduction with real-world analogy
- What is prompt injection (with simple examples)
- Types of attacks (direct vs indirect)
- Code examples showing vulnerable vs secure patterns (Python/JavaScript pseudocode)
- Defense strategies with implementation guidance
- Key takeaways section
Guidelines:
- 1500-2000 words
- Conversational but authoritative tone
- Explain jargon when first introduced
- Reference OWASP LLM Top 10
- No actual exploit code; focus on defense patterns
- Include spots for diagrams with descriptions
Code Review
DevelopmentView assembled prompt ▾
Review the following Node.js authentication module for a production financial transactions API.
Focus areas:
- Security vulnerabilities (OWASP ASVS compliance)
- GDPR data handling requirements
- Code maintainability
Output format:
| Severity | Line | Issue | Recommendation |
|----------|------|-------|----------------|
| Critical | ... | ... | ... |
For each finding:
- Specific line reference
- Clear explanation of the risk
- Code snippet showing the fix
Ignore: Code styling, formatting, naming conventions (handled by linter)
Research Summary
AcademicView assembled prompt ▾
Summarize recent advances (2022-2025) in adversarial machine learning for a PhD dissertation literature review chapter on AI robustness.
Structure:
1. Overview of the field evolution (2022-2025)
2. Key themes:
- Evasion attacks and defenses
- Data poisoning attacks
- Certified robustness methods
3. Seminal papers table: Paper | Authors | Year | Key Contribution | Citation count
4. Identified research gaps
5. Future directions
Guidelines:
- Academic writing style
- APA 7 citation format
- Only peer-reviewed sources; clearly mark preprints with [preprint]
- Include mathematical notation for key concepts
- Balanced perspective acknowledging limitations
The Complete Picture
Two frameworks, one unified AI security methodology.
AATMF
Offensive Framework
How attackers exploit LLM systems. 20 tactics, 240+ techniques for understanding adversarial AI threats.
Explore AATMF →P.R.O.M.P.T.
Defensive Framework
How to communicate effectively with LLMs. 6 principles for precise, reliable, and safe AI interactions.
You are hereUnderstanding both frameworks creates a complete picture: knowing how attacks work (AATMF) helps you design prompts that are both effective AND resistant to manipulation (P.R.O.M.P.T.).
Learn More
External Resources
Created by
Kai Aizen (SnailSploit)
GenAI Security Researcher | Creator of AATMF & P.R.O.M.P.T frameworks