Updating text

Introduction.py

@@ -23,7 +23,7 @@ def main():
 
         #### The SQL database used in this demo
         The database used in this demo is the Chinook database.
-        It is a sample database that represents a digital media store, including tables for artists, albums, media tracks, invoices and customers.
+        It is a sample database that represents a digital media store, including tables for artists, albums, media tracks, invoices, and customers.
 
         You can see the schema below:
         """
@@ -32,22 +32,22 @@ def main():
 
     st.markdown(
         """
-        #### What does LLMs have to do with this?
+        #### What do LLMs have to do with this?
         A large use case for large language models (LLM) is to generate SQL queries.
         This is a very useful feature, as it allows users to interact with databases without having to know SQL.
-        But this is also prone to SQL injections, as the users and by extension the LLMs, can generate malicious SQL queries.
+        But this is also prone to SQL injections, as the users can maliciously propt the LLM to generate harmful SQL queries.
         """
     )
 
     st.divider()
     st.markdown(
         """
-        #### The levels
+        #### The Levels
         Try to inject malicious SQL code to alter the SQL table, each level is more difficult than the previous one!
 
         - **Level 1**: You generate the SQL queries with the help of the LLM.
         - **Level 2**: The SQL queries are first checked by an LLM Safeguard, which detects and removes malicious SQL queries.
-        - **Level 3**: The only difference is that we are using a better LLM model, GPT-4, for the safeguard. Otherwise they are the same.
+        - **Level 3**: The only difference with Level 2 is that we are using a better LLM model, GPT-4, for the safeguard.
 
         Are you happy with your results? Submit the keys on the leaderboard to see how you compare to others!
         """