🤖 Building intelligent applications with Python • Generative AI • LLMs • RAG • Machine Learning

🎓 B.Tech in Information Technology

🤖 Focused on Generative AI & LLM Applications

🐍 Building AI-powered applications using Python

🧠 Exploring Machine Learning & Deep Learning

🔎 Working with RAG, Embeddings, Vector Databases & LLMs

💻 Interested in AI Engineering, Backend Development & Intelligent Applications

🌱 Continuously learning and building practical AI-powered solutions

What if your RAG system could decide when NOT to retrieve? 🤔

Built an intelligent Retrieval-Augmented Generation (RAG) application for document-based question answering using Python, LangChain, LLMs, ChromaDB, Embeddings, FastAPI and Streamlit.

🔹 Intelligent Query Routing

Classifies user queries as General or Document-related and dynamically decides whether vector retrieval is required, helping reduce unnecessary processing and latency.

🔹 Complete RAG Pipeline

📄 Documents
      ↓
✂️ Document Processing & Chunking
      ↓
🧠 Embeddings
      ↓
🗄️ ChromaDB / Vector Database
      ↓
🔎 Relevant Document Retrieval
      ↓
📚 Context Formation
      ↓
🤖 LLM Generation
      ↓
💬 Grounded Response + Source Citations

🔹 Context-Aware Generation

Retrieved document context is integrated with LLM prompts to generate relevant and grounded responses.

🔹 Source Citations

Generated responses include source citations, making information easier to verify.

🔹 FastAPI Backend

Developed a modular FastAPI backend to manage the application and RAG workflow.

🔹 Streamlit Interface

Created an interactive Streamlit chat interface for document-based conversations.

🔹 Deployment

Deployed the application using Render for real-world accessibility.

Python Generative AI LLMs RAG LangChain ChromaDB Embeddings Vector Database Prompt Engineering FastAPI Streamlit

Generative AI
      ↓
Large Language Models
      ↓
Prompt Engineering
      ↓
Embeddings & Vector Search
      ↓
Retrieval-Augmented Generation
      ↓
Context Engineering
      ↓
AI Agents
      ↓
Production AI Applications

An AI-powered virtual makeup application using Computer Vision and Deep Learning techniques.

Python Computer Vision Deep Learning U-Net Image Segmentation

A Machine Learning-based system for monitoring EV component health and detecting potential failures.

Python Machine Learning Random Forest XGBoost LSTM Isolation Forest

An AI-based solution for identifying the appropriate delivery post office using location and pincode-related information.

Python Machine Learning AI Data Processing

🏅 Oracle GenAI Professional Certificate

🏆 Smart India Hackathon 2025 — 2nd Rank (Inter-College)

🎤 Represented Barkatullah University in Singing at Yuva Utsav

🥇 National Debate Competition — 5th TechForSeva (All India)

💻 JPMorgan Chase Software Engineering Job Simulation — Forage

Learn. Build. Experiment. Repeat. 🚀

Working towards becoming a strong Generative AI / AI Engineer, building practical AI systems and continuously improving my expertise in LLMs, RAG, Machine Learning, AI Engineering and Software Development.

⭐ If you find my projects interesting, consider giving them a star!