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README.md

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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
-Workshop time: 8.00 am PST/11am pm EST - 2.00pm PST / 5pm EST
-Instructor: Sujee Maniyam, Node 51. See Instructor Profile [here](https://www.mongodb.com/developer/author/sujee-maniyam/). 
+- Registration Link: https://tinyurl.com/3tnfey9n
+- Cost: $300 for NET+ Subscribers, $400 for Internet2 members, $500 for non-members
+- Workshop time: 8.00 am PST/11am pm EST - 2.00pm PST / 5pm EST
+- Instructor: Sujee Maniyam, Node 51. See Instructor Profile [here](https://www.mongodb.com/developer/author/sujee-maniyam/). 
 
 ## 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.
+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
@@ -22,51 +21,50 @@ Introductory to Intermediate
 - 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)
+- Comfortable with python programming and notebooks
+- Access to Google Colab
+- A free subscription to a vector database like MongoDB Atlas
+- Using hosted models like chatGPT, Mistral will require a subscription
+
 
 ## Details
 ### 1 - Embeddings
-Understanding embeddings
-Various embedding models
-Semantic text search using embeddings
-evaluating a various embedding models
+- 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
+- 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
+- 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
+- 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
+- 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)
+- 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
+- Containerizing applications
+- Model serving
+- Scalability
 
 ### 8 - Workshop / Project
-Attendees will a sample application using data of interest, and LLM of their choice
+- Attendees will a sample application using data of interest, and LLM of their choice