RAG is becoming the standard to customize the output of LLMs for domain specific applications with data. Learn how to build RAG applications from scratch.

Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python

Master Spring AI from scratch — learn to build AI-powered Java applications with LLMs, OpenAI, Ollama, embeddings, vector databases (RAG), and production-ready features using Spring Boo

A conversational data assistant that converts natural language to SQL queries, built to learn LLMs, RAG, and NLU from scratch.

🚀 Build a complete RAG agent from scratch using Python. Hybrid retrieval (FAISS + BM25), cross-encoder reranking, query expansion, and grounded LLM responses — all without frameworks. Learn how RAG really works

This project demonstrates core RAG components such as data loaders, text splitters, vector embeddings, retrievers, and LLM-based response generation — built using tools like LangChain, ChromaDB, and OpenAI/GPT. Perfect for anyone looking to learn or prototype RAG systems from scratch.

Learn to build autonomous AI agents from scratch. This project covers the full lifecycle of Agentic AI development, including Local LLMs, Vector & Graph RAG, multi-agent orchestration, and voice capabilities using a modern Python stack.

A step-by-step journey into Generative AI from scratch. Explore LLMs, Prompt Engineering, RAG, LangChain, and open-source models with hands-on notebooks and practical examples. Learn how to build real-world GenAI apps using modern tools and frameworks.

A simple command-line RAG (Retieval Augmented Generation) chatbot application built from scratch. No external framework or library has been used except requests and scikit-learn modules. It uses a locally hosted LLM via Ollama to generate embedding and response to user's query based on relevant information from a text file.