Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.

An LLM-powered advanced RAG pipeline built from scratch

Hack the Act! is a RAG-based chatbot designed to demystify the European Union AI Act

Dedicated to simplifying the complexities of Retrieval-Augmented-Generation (RAG) for non-AI experts. Our mission is to provide accessible resources and clear explanations to help individuals understand the fundamentals of RAG and its applications in natural language processing.

A Comprehensive Guide to Mastering RAG for AI Engineering

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AI solution to demystify legal documents using Gemini and RAG

AI assistant using a RAG pipeline to demystify complex regulatory documents.

Demystifying LLMs: My Hands-On Journey Building a RAG System with Knowledge Graphs!

LangGraph Demystified: Comprehensive course materials covering LangGraph essentials, AI agents, tool-use, memory, routing, planning, human-in-the-loop, and RAG patterns

A RAG-based AI consultant using Hugging Face to demystify complex legal documents. Simplifies legalese and answers document-specific queries in real-time.

An under development legal document demystifier, with features like AI Simulator, Summary View, Risk Analysis, and many more. Uses gemini-1.5-flash model, built using RAG pipeline.

Generative AI for Demystifying Legal Documents uses a Retrieval-Augmented Generation (RAG) pipeline with ChromaDB, Neo4j, and LLMs to parse, retrieve, and explain complex legal or insurance documents in simple, human-readable language.

Entry for the Precision FDA Democratizing and Demystifying AI - GenAI Community Challenge. The tool is a rag based local llm setup capable of accurately answering questions using FDA data from the Cosmetic Guidance PDF

A ground-up implementation of K-Nearest Neighbors (KNN) vector search in raw code. This project reconstructs the mathematical and algorithmic foundations behind modern embedding-based retrieval systems and demystifies the core mechanics powering Retrieval-Augmented Generation (RAG).