diff --git a/tutorials/how-to-implement-rag/index.mdx b/tutorials/how-to-implement-rag/index.mdx
index 38ca4ba4a1..95f1c5fca9 100644
--- a/tutorials/how-to-implement-rag/index.mdx
+++ b/tutorials/how-to-implement-rag/index.mdx
@@ -40,7 +40,7 @@ Run the following command to install the required packages:
    ```sh
    pip install langchain psycopg2 python-dotenv
    ```
-### Step 2: Create a .env file
+### Create a .env file
 
 Create a .env file and add the following variables. These will store your API keys, database connection details, and other configuration values.
 
@@ -117,9 +117,9 @@ conn = psycopg2.connect(
 cur = conn.cursor()
    ```
 
-## Embeddings and Vector Store Setup
+## Embeddings and vector store setup
 
-### Import Required Modules
+### Import required modules
 
 ```python
 # rag.py
@@ -130,7 +130,7 @@ from langchain_postgres import PGVector
 
 ### Configure OpenAI Embeddings
 
-We will utilize the [OpenAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html) class from LangChain and store the embeddings in PostgreSQL using the PGVector integration.
+We will use the [OpenAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html) class from LangChain and store the embeddings in PostgreSQL using the PGVector integration.
 
 ```python
 # rag.py