diff --git a/README.md b/README.md
index d32e381..cf0c849 100644
--- a/README.md
+++ b/README.md
@@ -65,8 +65,9 @@ You need to install following tools to work on your local machine:
 - [Node.js LTS](https://nodejs.org/download/)
 - [Azure Developer CLI](https://aka.ms/azure-dev/install)
 - [Git](https://git-scm.com/downloads)
-<!-- - [Powershell 7+ (pwsh)](https://github.com/powershell/powershell) - For Windows users only.
-  - **Important**: Ensure you can run `pwsh.exe` from a PowerShell command. If this fails, you likely need to upgrade PowerShell. -->
+- [PowerShell 7+](https://github.com/powershell/powershell) _(for Windows users only)_
+  - **Important**: Ensure you can run `pwsh.exe` from a PowerShell command. If this fails, you likely need to upgrade PowerShell.
+  - Instead of Powershell, you can also use Git Bash or WSL to run the Azure Developer CLI commands.
 
 Then you can get the project code:
 
diff --git a/docs/quickstart.md b/docs/readme.md
similarity index 96%
rename from docs/quickstart.md
rename to docs/readme.md
index 8cf1b5c..27d9f0e 100644
--- a/docs/quickstart.md
+++ b/docs/readme.md
@@ -84,7 +84,7 @@ Our API is composed of two main endpoints:
 
 - `/chat`: This endpoint receives a list of messages, the last being the user query and returns a response generated by the LLM. It uses the documents stored in the database to generate the response. We use LangChain.js components to connect to the database, load the documents and perform a vector search after vectorizing the user query. After that, the most relevant documents are injected into the prompt, and we generate the response. While this process seems complex, LangChain.js does all the heavy lifting for us so we can focus on the application flow.
 
-The `/documents` endpoint is used to ingest the documents after the application is deployed, using `curl` commands (have a look at the `postup` hook in the `azure.yaml` file).
+The `/documents` endpoint is used to ingest the documents after the application is deployed by uploading the PDFs, using either `curl` commands or the Node.js script we built (have a look at the `postup` hook in the `azure.yaml` file).
 
 The web app is a simple chat interface that sends the user queries to the `/chat` endpoint and displays the responses.
 We use the [HTTP protocol for AI chat apps](https://github.com/Azure-Samples/ai-chat-app-protocol) to communicate between the web app and the API.
@@ -108,7 +108,8 @@ Here are some resources to learn more about the technologies used in this sample
 - [Azure Cosmos DB for MongoDB vCore](https://learn.microsoft.com/azure/cosmos-db/mongodb/vcore/)
 - [Ask YouTube: LangChain.js + Azure Quickstart sample](https://github.com/Azure-Samples/langchainjs-quickstart-demo)
 - [Revolutionize your Enterprise Data with ChatGPT: Next-gen Apps w/ Azure OpenAI and AI Search](https://aka.ms/entgptsearchblog)
-- [Find more Azure AI samples](https://github.com/Azure-Samples/azureai-samples)
+
+You can also find [more Azure AI samples here](https://github.com/Azure-Samples/azureai-samples).
 
 ## FAQ
 
diff --git a/packages/webapp/src/components/chat.ts b/packages/webapp/src/components/chat.ts
index fa546a2..5e1b9e6 100644
--- a/packages/webapp/src/components/chat.ts
+++ b/packages/webapp/src/components/chat.ts
@@ -399,6 +399,7 @@ export class ChatComponent extends LitElement {
     }
     svg {
       fill: currentColor;
+      width: 100%;
     }
     button {
       font-size: 1rem;