🤖 Building a Personalized Chatbot Powered by my Portfolio
Implementing a RAG model

I am an Industrial Engineer utilizing the power of python to gain deeper insights in data.
I am currently learning Deep learning with TensorFlow
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Implementing a RAG model

I am an Industrial Engineer utilizing the power of python to gain deeper insights in data.
I am currently learning Deep learning with TensorFlow
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I recently built an interactive A3 process improvement app using Claude Sonnet 4 - and here's the interesting part: the app itself uses Claude's API to analyze completed A3 documents. It's essentially Claude helping to build a tool that leverages Cla...

Comparing Mistral 7B vs. LLaMA-2 7B using HuggingFace

This took a bit of time to get the certificate, but circling back to follow up on a previous accomplishment — I officially completed an AI/ML Apprenticeship! [U.S. Department of Labor apprenticeship completion certificate for the AI/ML Fundamentals P...
A time-series forecasting and anomaly-detection tool

In my latest project, I set out to build a personalized chatbot that could answer questions based on documents about me—think of it as an AI-powered assistant trained on my bio, resume, and project work. The goal was to create something interactive and intelligent that could provide relevant, accurate responses whenever someone wanted to learn more about my experience or skills.
To bring this to life, I used LlamaIndex for document indexing and OpenAI’s language models for generating responses. I deployed the app using Gradio on Hugging Face Spaces, which provided a clean and accessible interface. The chatbot accepts questions and retrieves information from a curated set of documents I uploaded, offering a live demo of how retrieval-augmented generation (RAG) can create powerful personal or professional tools.
Check it out here