Building reliable AI systems through rigorous testing and automation
Advanced validation for Large Language Models with RAG, MCP, and safety testing
Impact: 23% accuracy improvement • 60% faster testing • 3 critical safety violations prevented
Tech: JavaScript/Node.js, AI APIs, RAG, MCP
Live Demo | Documentation | Case Studies
Gradual AI integration for enterprise systems without disruption
Impact: 40% faster processing • 60% fraud reduction • Zero downtime migration
Tech: Python, Legacy System Integration, AI/ML Pipeline
Framework Details | Assessment Tool
Ethical AI-powered automation for career management
Impact: 60% time reduction • 85% job matching accuracy • Improved application quality
Tech: Python, Playwright, AI/ML, React/TypeScript
Project Details | Demo Screenshots
Systematic quantitative trading with risk management
Performance: +127% total return • 1.67 Sharpe ratio • 64% win rate
Tech: Python, pandas, Statistical Analysis, Risk Management
Strategy Details | Implementation
Test your skills at distinguishing AI-generated code from human-written code
Can you spot the difference between code written by AI and code written by humans? This interactive game presents real code snippets and challenges you to identify their origin. Learn the subtle patterns that distinguish AI coding style from human creativity and problem-solving approaches.
Features:
- 6 diverse code examples from simple functions to complex implementations
- Real-time scoring and accuracy tracking
- Educational explanations for each code snippet
- Mobile-responsive futuristic design
- No registration required - jump right in!
Challenge yourself: Can you achieve 80%+ accuracy and earn the "AI Code Detective" title?
- Projects Deployed: 4 production systems
- Performance Improvement: 23-60% across projects
- Testing Coverage: 85%+ automated validation
- AI Frameworks: RAG, MCP, LLM testing, safety validation
Found this useful? Here's how you can help:
- Star the repo to show support
- Report issues you encounter
- Suggest improvements via issues
- Share with your network
- Issues: Join the conversation about AI-First development
- Issues: Report bugs or request features
- Contributors: See who's helping build this project
- Prompt Engineering Guide - Master effective AI prompting techniques
- AI Workflow Integration - Integrate AI into daily development workflows
- AI-First Principles - Core philosophy and development approach
- AI Adoption Roadmap - Step-by-step guide for teams adopting AI
New to AI-First development? Start here: START HERE Guide
Want to customize this template? See: Customization Guide
├── llm-guardian/ # LLM Testing Framework (Flagship Project)
│ ├── README.md # Framework documentation
│ ├── demo.html # Interactive demonstrations
│ ├── src/ # Core framework code
│ ├── examples/ # Usage examples
│ └── case-studies/ # Real-world implementations
├── legacy-ai-bridge/ # Enterprise AI integration framework
│ ├── README.md # Framework overview
│ ├── assessment-template.md # Legacy system evaluation
│ └── implementation-guide.md # Step-by-step technical guide
├── job-search-automation/ # AI automation project
│ ├── README.md # Project documentation
│ └── demo-screenshots.md # Visual demonstrations
├── algorithmic-trading/ # Quantitative trading project
│ ├── README.md # Strategy overview and results
│ └── strategy-implementation.md # Technical implementation
├── qa-prompts/ # AI prompt library for QA/SDET
│ ├── README.md # Library overview
│ └── prompts/ # Categorized prompt collections
├── research/ # AI Research & Jupyter Notebooks
│ ├── index.html # Research landing page
│ └── notebooks/ # Jupyter notebook collection
│ ├── llm-testing-analysis.ipynb # LLM testing research
│ ├── llm-testing-analysis.html # HTML viewer
│ ├── ai-safety-metrics.ipynb # AI safety research
│ ├── ai-safety-metrics.html # HTML viewer
│ ├── automated-testing-patterns.ipynb # Testing patterns research
│ └── automated-testing-patterns.html # HTML viewer
├── docs/ # Learning resources and guides
│ ├── PROMPT-ENGINEERING-GUIDE.md
│ ├── AI-WORKFLOW-INTEGRATION.md
│ ├── AI-FIRST-MANIFESTO.md
│ ├── AI-ADOPTION-ROADMAP.md
│ ├── START-HERE.md
│ └── CUSTOMIZATION.md
├── learn/ # Interactive learning hub
│ └── index.html # Learning portal
├── .github/ # GitHub configuration
│ └── workflows/ # CI/CD pipelines
└── images/ # Assets and media
This portfolio demonstrates AI-First development practices using advanced AI systems:
- Rapid Prototyping: Complete portfolio architecture designed and implemented in 1-2 days instead of 2-3 weeks
- AI-Assisted Development: Leveraged multiple AI systems for code generation, optimization, and rapid iteration
- Human-AI Collaboration: Strategic decisions, domain expertise, and quality control maintained by human developer
- Efficiency Gains: ~10x faster development cycle through intelligent automation and AI pair programming
- Technical Partnership: Advanced AI systems as development accelerators and code generation partners
This project was built using AI-First development practices with:
- Cursor AI Agentic Mode - Advanced code generation and pair programming
- Claude 4 Sonnet - Architecture planning, documentation, and complex reasoning
- DeepSeek AI - Rapid iteration and optimization support
Every technique in our guides was used to build this portfolio:
- Complete HTML/CSS generation with AI assistance for rapid iteration
- Advanced AI frameworks (RAG, MCP, LLM testing) implemented with AI assistance
- Production-ready CI/CD pipeline configured with AI guidance
Perfect for: Developers wanting to 10x their productivity, QA engineers transitioning to AI-first practices, and teams adopting AI-assisted development workflows.
MIT License - feel free to use this template for your own portfolio!
@portfolio{elamcb2025,
address = {USA},
author = {Elena Mereanu},
title = {{AI-First Quality Engineering Portfolio}},
url = {https://elamcb.github.io},
linkedin = {https://linkedin.com/in/elenamereanu},
github = {https://github.com/ElaMCB},
year = {2025}
}