╔═══════════════════════════════════════════════════════════════╗
║ ║
║ UNIVERSAL SYSTEM PROMPT ENGINEERING - PHASE 1 ║
║ Production-Grade AI System Prompts Framework ║
║ ║
║ 10 Knowledge Bases | 200+ Q&A Pairs | Zero-Cost Deploy ║
║ ║
╚═══════════════════════════════════════════════════════════════╝
A production-grade framework for building, securing, testing, and scaling AI system prompts. 10 comprehensive knowledge bases covering everything from persona anchoring to drift detection, backed by 2024-2026 research and practical implementation patterns.
Not a collection of prompt templates. Actual engineering methodology for enterprise AI systems.
# Clone repository
git clone https://github.com/VibeCodingLabs/universal-system-prompt-engineering.git
cd universal-system-prompt-engineering
# Run automated setup
bash setup.sh
# Verify services
docker-compose ps
# Open interface
open http://localhost:3000Everything works locally. Zero cost. No API keys needed.
| KB | Title | Focus | Your Use Case |
|---|---|---|---|
| 01 | Persona Anchoring | Behavioral consistency | Keep AI in character |
| 02 | Delimiters & Security | Jailbreak prevention | Prevent prompt injection |
| 03 | Structured Output | Format validation | JSON/YAML/CSV reliability |
| 04 | Reasoning Frameworks | Chain-of-Thought | Understand AI thinking |
| 05 | Safety Guardrails | Content filtering | No harmful outputs |
| 06 | Privacy Compliance | GDPR/CCPA | Legal compliance |
| 07 | Determinism | Reproducibility | Audit-ready outputs |
| 08 | Cross-Model Transfer | PromptBridge | Claude/Gemini compatibility |
| 09 | Context Management | RAG & Token budgeting | 50% cost savings |
| 10 | Testing & Drift | Monitoring & validation | Production reliability |
Get started: Read SUMMARY.md (2 min) → then KB 01-03 sequentially
Production AI System =
Persona (who)
+ Security (walls)
+ Structured Output (format)
+ Reasoning (how it thinks)
+ Guardrails (what it won't do)
+ Privacy (data protection)
+ Determinism (reproducibility)
+ Multi-Model (Claude/Gemini)
+ Optimization (cost)
+ Testing (reliability)
- ✅ Faster inference
- ✅ 30-50% cheaper
- ✅ Production-ready
- ✅ Auditable & compliant
- ✅ Portable across models
- ✅ Systematically tested
Traditional Prompt Engineering:
- Guesswork ("Let me try adding more examples")
- Brittle (breaks with slight input changes)
- Non-deterministic (inconsistent outputs)
- Insecure (vulnerable to jailbreaks)
- Expensive (wasteful token usage)
- Non-portable (one model, rewrite for another)
This Framework:
- Evidence-based (2024-2026 research)
- Robust (systematic testing)
- Deterministic (reproducible)
- Hardened (layered security)
- Optimized (strategic token allocation)
- Portable (works cross-model)
- Device: HP ENVY x360 16GB RAM
- Inference: CPU-only (models run locally)
- Cost: $0/month (no API subscriptions)
- Setup Time: ~30 minutes
- Ollama (local LLM engine)
- ChromaDB (vector database for RAG)
- Open WebUI (chat interface)
- n8n (workflow automation)
- Full Python stack (PydanticAI, FastAPI)
All free. All local. All open-source.
Basic ($99) - KB 01 + 03 only
- Persona + structured output
- 3-day delivery
Pro ($299) - KB 01, 03, 05, 10
- Adds safety + testing
- 7-day delivery
Enterprise ($999) - All KB 01-10
- Full framework implementation
- Custom deployment
- 30-day support
- Others: "I'll write you a prompt"
- You: "I'll architect a production AI system with deterministic outputs, security hardening, cost optimization, and automated testing"
Fiverr Gig Title:
"I'll build production-grade AI system prompts using evidence-based engineering framework"
| File | Purpose |
|---|---|
| README.md | This file (overview) |
| SUMMARY.md | 1-page quick reference |
| INDEX.md | Navigation & cross-references |
| GUIDE.md | Step-by-step implementation (with copy-paste commands) |
| DEPLOYMENT.md | Production deployment checklist |
| OPSEC.md | Security hardening |
| SECURITY.md | Threat model |
| CONTRIBUTING.md | How to contribute |
| KB.md* | 10 comprehensive knowledge bases |
- 🔗 Website: https://www.phantom-digital.com
- 💼 LinkedIn: @VibeCodingLabs
- 🎯 Fiverr: @VibeCodingLabs
- 📈 Upwork: @VibeCodingLabs
- 🎁 Gumroad: Prompt Engineering Templates
- ☕ Buy Me Coffee: Support Development
- 💝 Payhip: Digital Products
╔════════════════════════════════════════════╗
║ PHANTOM DIGITAL / PD ║
║ Universal System Prompt Engineering ║
║ Enterprise AI Systems Framework ║
╚════════════════════════════════════════════╝
Website: https://www.phantom-digital.com
Logo Colors:
- Phantom Blue:
#0b1c2d - Cyber Cyan:
#00e5ff - Linear Gradient: Blue → Cyan
#PromptEngineering #LLM #AISystemDesign #SystemPrompts
#GPT4 #Claude #Gemini #LocalLLM #Ollama #ChromaDB
#Determinism #Safety #Security #Privacy #GDPR
#CostOptimization #RAG #Testing #ProductionAI
#OpenSource #Docker #n8n #Fiverr #FreelanceAI
- Reproducibility: Deterministic AI inference & audit trails
- Scalability: Multi-model architecture & cross-platform compatibility
- Economics: Token budgeting & cost optimization strategies
Help maintain & expand this framework:
- ☕ Coffee Sponsor ($5) - One-time support
- 🚀 Launch Sponsor ($50) - Monthly recurring
- 🏢 Enterprise Sponsor ($500+) - Custom features
This is not investment or legal advice. The framework provides technical guidance for building AI systems. You remain responsible for:
- Compliance with applicable laws (GDPR, CCPA, etc.)
- Safety & security of your implementation
- Content policies of third-party APIs
- Testing before production deployment
Use at your own risk. No warranties express or implied.
Want to improve this framework?
- Fork the repository
- Create a feature branch
- Make improvements
- Submit a pull request
See CONTRIBUTING.md for detailed guidelines.
- 10 comprehensive knowledge bases
- 200+ training Q&A pairs
- 40+ code examples
- 60+ tool recommendations
- Zero cost to deploy
- <30 minutes to full setup
- Quick Start
- Knowledge Bases
- Framework Methodology
- Local Implementation
- Monetization
- Documentation
- Contributing
- License
MIT License - Free to use, modify, and distribute.
See LICENSE file for details.
Built by VibeCodingLabs | 2024-2026
Research informed by:
- OpenAI, Anthropic, Google DeepMind publications
- Mechanistic Interpretability research
- Community prompt engineering practices
- Production deployment experiences
- Read:
SUMMARY.md(2 min) - Setup:
bash setup.sh(30 min) - Learn:
KB*.md(sequential) - Build: Follow
GUIDE.md - Deploy: Use
DEPLOYMENT.md - Monetize: Create Fiverr gig
- Scale: Grow from there
Time to first paid gig: 4-6 hours
╔═══════════════════════════════════════════════════════════════╗
║ ║
║ Made with ❤️ by VibeCodingLabs ║
║ Universal System Prompt Engineering v1.0 ║
║ Production-Ready AI System Prompts Framework ║
║ ║
║ GitHub: VibeCodingLabs/universal-system-prompt-engineering ║
║ Website: https://www.phantom-digital.com ║
║ ║
╚═══════════════════════════════════════════════════════════════╝
Questions? Start with INDEX.md for navigation or GUIDE.md for hands-on implementation.