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+# 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
+   ```bash
+   git clone https://github.com/zainabahmed4626-lab/AI-Engineering.git
+   cd AI-Engineering
+   ```
+
+2. Create and activate a virtual environment
+   ```bash
+   python -m venv .venv
+   # Windows PowerShell
+   .venv\Scripts\Activate.ps1
+   ```
+
+3. Install dependencies
+   ```bash
+   pip install -r requirements.txt
+   pip install numpy matplotlib scikit-learn plotly nbconvert ipykernel
+   ```
+
+4. Configure environment variables
+   ```env
+   OPENAI_API_KEY=your_key_here
+   ```
+
+5. Launch notebooks
+   ```bash
+   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](https://github.com/zainabahmed4626-lab)
+- Portfolio/Contact: [resume-zainab.lovable.app](https://resume-zainab.lovable.app/#contact)