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+# Developing a Web-based Q&A system using a RAG-based approach to LLMs - June 25, 2024
+
+## Course Information 
+Registration Link: https://tinyurl.com/3tnfey9n
+
+
+## Introduction
+We have all seen how powerful and impactful Large Language Models (LLMs) like ChatGPT are.  These very powerful AI models are enabling a new generation of conversational 
+applications. This 6-hour hands-on workshop introduces the audience to LLMs, data augmentation using the Retrieval Generation Augmentation (RAG) approach, and how to how to 
+build web using open-source tools.
+
+## Skills Level
+Introductory to Intermediate
+
+## What You Will Learn
+- Embeddings and embedding models; searching text for meanings / context using embeddings
+- Vector databases and performing text search with vector databases
+- LLMs and using them (via API and locally)
+- Developing applications using LLM
+- Developing RAG applications
+- Deploying containerized applications
+
+## Prerequisites
+Comfortable with python programming and notebooks
+Have Python development environment locally or have access to Google Colab or other Jupyter environments
+A free subscription to a vector database like MongoDB Atlas
+Using hosted models like chatGPT, Mistral will require a subscription
+To run a local LLM, we recommend a GPU system (a laptop with GPU or Google Colab or cloud instance with GPU)
+
+## Details
+### 1 - Embeddings
+Understanding embeddings
+Various embedding models
+Semantic text search using embeddings
+evaluating a various embedding models
+
+### 2 - Vector Databases
+Introduction to vector databases
+getting started with MongoDB Atlas
+Loading data into database and populate embeddings
+Vector search with database and embeddings
+
+### 3 - LLMs
+Introduction to LLMs, the eco system
+Access LLMs via API
+Run LLMs locally using llama-cpp, oolama, lm-studio, jen
+Experiment with different LLMs
+
+### 4 - Running LLMs
+We will use a framework like llama-cpp, llama-index and Langchain to run local LLMs
+
+Run a local LLM.  Experiment with various open LLMs like Mistral, Llama
+
+### 5 - Develop a custom application using LLM
+Use frameworks like streamlit, flask to build sample applications
+
+### 6 - Building RAG Applications
+Here we will query PDF documents using LLMs
+Index documents with embeddings.  Use various embedding models (OpenAI, Mistral, open source models)
+query the documents using various LLMs (OpenAI, Mistral, LLama)
+
+### 7 - Deploying RAG Applications
+containerizing applications
+model serving
+scale
+
+### 8 - Workshop / Project
+Attendees will a sample application using data of interest, and LLM of their choice
+
+
+