Update RAG LP

Author: annietllnd

content/learning-paths/servers-and-cloud-computing/rag/_index.md

@@ -3,11 +3,11 @@ title: Deploy a RAG-based Chatbot with llama-cpp-python using KleidiAI on Arm Se
 
 minutes_to_complete: 45
 
-who_is_this_for: This Learning Path is for software developers, ML engineers, and those looking to deploy production-ready LLM chatbots with RAG capabilities, knowledge base integration, and performance optimization for Arm Architecture.
+who_is_this_for: This Learning Path is for software developers, ML engineers, and those looking to deploy production-ready LLM chatbots with Retrieval Augmented Generation (RAG) capabilities, knowledge base integration, and performance optimization for Arm Architecture.
 
 learning_objectives:
     - Set up llama-cpp-python optimized for Arm servers.
-    - Implement RAG architecture using the FAISS vector database.
+    - Implement RAG architecture using the Facebook AI Similarity Search (FAISS) vector database.
     - Optimize model performance through 4-bit quantization.
     - Build a web interface for document upload and chat.
     - Monitor and analyze inference performance metrics.

content/learning-paths/servers-and-cloud-computing/rag/chatbot.md

@@ -13,9 +13,14 @@ Open the web application in your browser using either the local URL or the exter
 http://localhost:8501 or http://75.101.253.177:8501
 ```
 
+{{% notice Note %}}
+
+To access the links you may need to allow inbound TCP traffic in your instance's security rules. Always review these permissions with caution as they may introduce security vulnerabilities.
+
+{{% /notice %}}
 ## Upload a PDF File and Create a New Index
 
-Now you can upload a PDF file in the web browser by selecting the **Create New Store** option. 
+Now you can upload a PDF file in the web browser by selecting the **Create New Store** option.
 
 Follow these steps to create a new index:
 

content/learning-paths/servers-and-cloud-computing/rag/rag_llm.md

@@ -14,7 +14,7 @@ This learning path demonstrates how to build and deploy a Retrieval Augmented Ge
 
 ## Overview
 
-In this Learning Path, you learn how to build a Retrieval Augmented Generation (RAG) chatbot using llama-cpp-python, a Python binding for llama.cpp that enables efficient LLM inference on Arm CPUs.
+In this Learning Path, you learn how to build a RAG chatbot using llama-cpp-python, a Python binding for llama.cpp that enables efficient LLM inference on Arm CPUs.
 
 The tutorial demonstrates how to integrate the FAISS vector database with the Llama-3.1-8B model for document retrieval, while leveraging llama-cpp-python's optimized C++ backend for high-performance inference.