Build a transformer-based LLM from scratch using Python and PyTorch. Master tokenization, positional encodings, multi-head self-attention, and autoregressive model training.
Learn how to fine-tune an LLM (DistilGPT-2) from scratch using Python and Hugging Face Transformers. Covers tokenization, dataset preparation, Trainer API training, and inference.
Watch in-depth system architectures, step-by-step implementation tutorials, and code reviews.
Build a custom RAG PDF chatbot using LangChain, ChromaDB, and Google Gemini API to perform fast semantic retrieval and generate grounded, context-aware answers.
Build a full-stack Agentic AI Chatbot using LangGraph, LangChain, Groq (LLaMA 3), OpenAI GPT-4o, Tavily Search, FastAPI, and Streamlit for real-time web-augmented reasoning.
Build a multilingual AI chatbot using Sarvam AI and Streamlit. Supports Indian languages like Hindi, Gujarati, Bengali, and Kannada with native LLM reasoning and real-time translation.
Build a modular, full-stack RAG PDF chatbot from scratch using LangChain, FastAPI, Streamlit, ChromaDB, and Groq (LLaMA 3) with clean frontend-backend separation and sub-second inference.
Automate job role validation between structured XML files and unstructured PDFs using Google Gemini, Pinecone vector search, PyMuPDF, and fuzzy string algorithms (Levenshtein Distance & Ratcliff-Obers
Build a full-stack AI agent with LangGraph, Groq LLaMA 3, Pinecone, and Tavily Search API. Features dynamic routing between PDF-grounded RAG and real-time web search with a FastAPI and Streamlit archi
Build a Multimodal RAG pipeline using PyMuPDF, Cohere embed-v4.0, ChromaDB, and Gemini 2.5 Flash with LangChain LCEL to query both text and visual diagrams from PDFs.
Build a role-based medical chatbot using FastAPI, Groq LLaMA 3, Pinecone, and MongoDB Atlas. Features document-grounded RAG with strict RBAC for Doctors, Nurses, and Patients.
Build a secure, real-time medical report diagnosis app using FastAPI, Streamlit, LangChain, Pinecone, and Groq. Features RAG for accurate insights and RBAC for role management.
Fix RAG hallucinations using HyDE. Bridge the semantic query-document gap with FastAPI, Groq LLaMA, Gemini embeddings, and Pinecone, boosting similarity from 0.69 to 0.85.
I Teaching Assistant is a production-ready, full-stack Generative AI application designed to streamline classroom learning through Retrieval-Augmented Generation (RAG) and automated evaluation
Discussion on Traditional RAG vs. Vectorless RAG, covering how both approaches work, their key differences, advantages and limitations, and most importantly, when to use each approach based on the use
Fundamental concepts regarding LangGraph and concept explanation
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