| # AI Engineering Portfolio |
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| 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. |
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| ## About This Repository |
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| 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. |
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| ## Core Skills Demonstrated |
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| - 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 |
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| ## Featured Work |
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| ### 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 |
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| ### 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 |
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| ### 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 |
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| ### 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 |
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| ## Tech Stack |
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| - Python |
| - OpenAI API |
| - Jupyter Notebook |
| - NumPy, Matplotlib, scikit-learn |
| - Plotly |
| - Gradio |
| - python-dotenv |
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| ## Quick Start |
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| 1. Clone the repository |
| ```bash |
| git clone https://github.com/zainabahmed4626-lab/AI-Engineering.git |
| cd AI-Engineering |
| ``` |
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| 2. Create and activate a virtual environment |
| ```bash |
| python -m venv .venv |
| # Windows PowerShell |
| .venv\Scripts\Activate.ps1 |
| ``` |
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| 3. Install dependencies |
| ```bash |
| pip install -r requirements.txt |
| pip install numpy matplotlib scikit-learn plotly nbconvert ipykernel |
| ``` |
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| 4. Configure environment variables |
| ```env |
| OPENAI_API_KEY=your_key_here |
| ``` |
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| 5. Launch notebooks |
| ```bash |
| jupyter notebook |
| ``` |
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| ## Project Structure |
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| - `ai_env/Ai_Engineering_Part1/` — main notebook experiments and prototypes |
| - `requirements.txt` — base dependencies |
| - `.env` — local environment variables (not for commit) |
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| ## Notes for Reviewers |
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| - 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. |
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| ## Contact |
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| - GitHub: [zainabahmed4626-lab](https://github.com/zainabahmed4626-lab) |
| - Portfolio/Contact: [resume-zainab.lovable.app](https://resume-zainab.lovable.app/#contact) |
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