Zainab4626/AI-Engineering

AI Engineering Portfolio

Applied AI and Generative AI project portfolio focused on practical implementation: retrieval-augmented generation (RAG), tool calling, prompt strategy, conversation memory, and interactive app workflows.

About This Repository

This repository documents hands-on AI engineering work through executable notebooks and supporting scripts. The goal is to demonstrate practical system design and implementation skills for real-world GenAI applications.

Core Skills Demonstrated

• LLM application development with OpenAI APIs
• Retrieval-Augmented Generation (RAG) pipelines
• Embeddings, chunking strategies, and semantic search setup
• Tool-calling and dynamic context orchestration patterns
• Prompt design and system-vs-user instruction control
• Conversational memory and context management
• Lightweight app prototyping with Gradio

Featured Work

RAG Implementation and Visualization

• ai_env/Ai_Engineering_Part1/rag1.ipynb
  • Implements text chunking with overlap and boundary-aware splitting
  • Generates embeddings (text-embedding-3-small)
  • Visualizes semantic structure with 2D/3D t-SNE clustering
  • Includes cluster-level interpretation output for explainability

Dynamic Context + Tool Calling Architecture

• ai_env/Ai_Engineering_Part1/digital-twin-arch1-dynamic-context-toolcallingZ1.ipynb
• ai_env/Ai_Engineering_Part1/digital-twin-arch2-basic-tool-calling.ipynb
  • Explores architecture evolution from basic to dynamic tool-calling patterns
  • Demonstrates practical context assembly for agent-like behavior

Prompting, Memory, and Agent Foundations

• ai_env/Ai_Engineering_Part1/system-vs-user-prompt.ipynb
• ai_env/Ai_Engineering_Part1/conversation-history.ipynb
• ai_env/Ai_Engineering_Part1/tool_callling.ipynb
  • Focus on instruction hierarchy, session memory, and safe tool invocation

App and Workflow Prototypes

• ai_env/Ai_Engineering_Part1/gradio.ipynb
• ai_env/Ai_Engineering_Part1/gradio_mcp_chat.py
• ai_env/Ai_Engineering_Part1/run_digital_twin_e2e.py
  • Demonstrates prototyping and basic end-to-end execution workflows

Tech Stack

• Python
• OpenAI API
• Jupyter Notebook
• NumPy, Matplotlib, scikit-learn
• Plotly
• Gradio
• python-dotenv

Quick Start

1. Clone the repository

   git clone https://github.com/zainabahmed4626-lab/AI-Engineering.git
   cd AI-Engineering
   

2. Create and activate a virtual environment

   python -m venv .venv
   # Windows PowerShell
   .venv\Scripts\Activate.ps1
   

3. Install dependencies

   pip install -r requirements.txt
   pip install numpy matplotlib scikit-learn plotly nbconvert ipykernel
   

4. Configure environment variables

   OPENAI_API_KEY=your_key_here
   

5. Launch notebooks

   jupyter notebook
   

Project Structure

• ai_env/Ai_Engineering_Part1/ — main notebook experiments and prototypes
• requirements.txt — base dependencies
• .env — local environment variables (not for commit)

Notes for Reviewers

• Notebooks are organized to show iterative engineering progress from fundamentals to architecture-level patterns.
• Several notebooks include execution-ready code and visualization output that can be run locally.
• This repo emphasizes implementation clarity and practical experimentation over framework-heavy abstractions.

Contact

• GitHub: zainabahmed4626-lab
• Portfolio/Contact: resume-zainab.lovable.app