PR fixes

Author: aamini7

scenarios/AksKaito/README.md

@@ -9,16 +9,6 @@ ms.author: schaffererin
 
 ---
 
-## Quickstart: Create a Linux virtual machine with the Azure CLI on Azure
-
-**Applies to:** :heavy_check_mark: Linux VMs
-
-[![Deploy to Azure](https://aka.ms/deploytoazurebutton)](https://go.microsoft.com/fwlink/?linkid=2262692)
-
-This quickstart shows you how to use the Azure CLI to deploy a Linux virtual machine (VM) in Azure. The Azure CLI is used to create and manage Azure resources via either the command line or scripts.
-
-If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/?WT.mc_id=A261C142F) before you begin.
-
 ## Deploy an AI model on Azure Kubernetes Service (AKS) with the AI toolchain operator (preview)
 
 The AI toolchain operator (KAITO) is a managed add-on for AKS that simplifies the experience of running OSS AI models on your AKS clusters. The AI toolchain operator automatically provisions the necessary GPU nodes and sets up the associated inference server as an endpoint server to your AI models. Using this add-on reduces your onboarding time and enables you to focus on AI model usage and development rather than infrastructure setup.

scenarios/PostgresRagLlmDemo/README.md

@@ -8,6 +8,16 @@ ms.author: ariaamini
 ms.custom: innovation-engine, linux-related-content 
 ---
 
+## Introduction
+
+In this doc, we go over how to host the infrastructure required to run a basic LLM model with RAG capabilities on Azure.
+We first set up a Postgres database capable of storing vector embeddings for documents/knowledge files that we want to use to
+augment our queries. We then create an Azure OpenAI deployment capable of generating embeddings and answering questions using the latest 'gpt-4-turbo' model.
+We then use a python script to fill our postgres database with embeddings from a sample "knowledge.txt" file containing information about an imaginary
+resource called 'Zytonium'. Once the database is filled with those embeddings, we use the same python script to answer any
+questions we have about 'Zytonium'. The script will search the database for relevant information for our query using an embeddings search and
+then augment our query with that relevant information before being sent our LLM to answer.
+
 ## Set up resource group
 
 Set up a resource group with a random ID.