diff --git a/docs/swarms/RAG/qdrant_rag.md b/docs/swarms/RAG/qdrant_rag.md
index 858fcfbe..d223ddfe 100644
--- a/docs/swarms/RAG/qdrant_rag.md
+++ b/docs/swarms/RAG/qdrant_rag.md
@@ -19,13 +19,13 @@ pip install qdrant-client fastembed swarms-memory litellm
 
 ## Tutorial Steps
 
-1. First, install the latest version of Swarms:
+1. **Install Swarms**: First, install the latest version of Swarms:
 
    ```bash
    pip3 install -U swarms
    ```
 
-2. Set up your environment variables in a `.env` file:
+2. **Environment Setup**: Set up your environment variables in a `.env` file:
 
    ```plaintext
    OPENAI_API_KEY="your-api-key-here"
@@ -34,16 +34,16 @@ pip install qdrant-client fastembed swarms-memory litellm
    WORKSPACE_DIR="agent_workspace"
    ```
 
-3. Choose your Qdrant deployment option:
+3. **Choose Deployment**: Select your Qdrant deployment option:
    - **In-memory**: For testing and development (data is not persisted)
    - **Local server**: For production deployments with persistent storage
    - **Qdrant Cloud**: Managed cloud service (recommended for production)
 
-4. Configure the vector database wrapper with your preferred embedding model and collection settings
+4. **Configure Database**: Set up the vector database wrapper with your preferred embedding model and collection settings
 
-5. Add documents using individual or batch processing methods
+5. **Add Documents**: Load documents using individual or batch processing methods
 
-6. Create your agent with RAG capabilities and start querying
+6. **Create Agent**: Initialize your agent with RAG capabilities and start querying
 
 ## Code