diff --git a/docs/docs/administration/playground.mdx b/docs/docs/administration/playground.mdx
index 28621b626..13d16422a 100644
--- a/docs/docs/administration/playground.mdx
+++ b/docs/docs/administration/playground.mdx
@@ -35,3 +35,21 @@ As long as you have a flow's environment variables set, you can run it by clicki
 >
   <ReactPlayer playing controls url="/videos/langflow_playground.mp4" />
 </div>
+
+## Playground I/O
+
+The Playground's appearance changes depending on what components are in your canvas.
+
+Adding or removing any of the below components modifies your Playground so you can monitor the inputs and outputs.
+
+* Chat Input
+* Text Input
+* Chat Output
+* Text Output
+* Records Output
+* Inspect Memory
+
+You can also select **Options** > **Logs** to see your flow's logs.
+
+For more information, see [Inputs and Outputs](../components/inputs-and-outputs.mdx).
+
diff --git a/docs/docs/getting-started/install-langflow.mdx b/docs/docs/getting-started/install-langflow.mdx
index e769aae01..09a4f46dc 100644
--- a/docs/docs/getting-started/install-langflow.mdx
+++ b/docs/docs/getting-started/install-langflow.mdx
@@ -6,13 +6,10 @@ import Admonition from "@theme/Admonition";
 # 📦 Install Langflow
 
 <Admonition type="info">
-  Langflow v1.0 alpha is also available in HuggingFace Spaces. [Clone the space
-  using this
-  link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true),
-  to create your own Langflow workspace in minutes.
+  Langflow **requires** Python version 3.10 or greater.
 </Admonition>
 
-Langflow requires [Python >=3.10](https://www.python.org/downloads/release/python-3100/) and [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/) to be installed on your system.
+Langflow **requires** [Python >=3.10](https://www.python.org/downloads/release/python-3100/) and [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/) to be installed on your system.
 
 Install Langflow with pip:
 
diff --git a/docs/docs/getting-started/quickstart.mdx b/docs/docs/getting-started/quickstart.mdx
index 3f02db27f..544b29b18 100644
--- a/docs/docs/getting-started/quickstart.mdx
+++ b/docs/docs/getting-started/quickstart.mdx
@@ -10,6 +10,8 @@ This guide demonstrates how to build a basic prompt flow and modify that prompt
 
 ## Prerequisites
 
+- [Python >=3.10](https://www.python.org/downloads/release/python-3100/) and [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/)
+
 - [Langflow installed and running](./install-langflow.mdx)
 
 - [OpenAI API key](https://platform.openai.com)
diff --git a/docs/docs/tutorials/rag-with-astradb.mdx b/docs/docs/tutorials/rag-with-astradb.mdx
index 9bb813f55..d8dc38a50 100644
--- a/docs/docs/tutorials/rag-with-astradb.mdx
+++ b/docs/docs/tutorials/rag-with-astradb.mdx
@@ -13,7 +13,7 @@ In this guide, we will use Astra DB as a vector store to store and retrieve the
 
 <Admonition type="tip">
   This guide assumes that you have Langflow up and running. If you are new to
-  Langflow, you can check out the [Getting Started](/) guide.
+  Langflow, you can check out the [Getting Started](../getting-started/install-langflow.mdx) guide.
 </Admonition>
 
 TLDR;
@@ -75,9 +75,11 @@ Now we are all set to start building our RAG application using Astra DB and Lang
 
 If you haven't already, now is the time to launch Langflow. To make things easier, you can duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you.
 
-## Open the Vector Store RAG Project
+## Open the Vector Store RAG Project in Langflow
 
-To get started, click on the **New Project** button and look for the **Vector Store RAG** project. This will open a starter project with the necessary components to run a RAG application using Astra DB.
+Run Langflow and open the UI.
+
+In the Langflow dashboard, click the **New Project** button and select the **Vector Store RAG** project. This will open a starter project with the necessary components to run a RAG application using Astra DB.
 
 This project consists of two flows. The simpler one is the **Ingestion Flow** which is responsible for ingesting the documents into the Astra DB database.