Update app.py

app.py

@@ -69,8 +69,8 @@ description = """<strong>Overview:</strong> The app demonstrates how to use a La
                  <strong>Instructions:</strong> Enter an OpenAI API key and perform LLM use cases (semantic search, sentiment analysis, summarization, translation, etc.) on 
                  a <a href='https://www.youtube.com/watch?v=--khbXchTeE'>short video of GPT-4</a>.
                  <ul style="list-style-type:square;">
-                 <li>Set "Retrieval Augmented Generation" to "False" and submit prompt "what is gpt-4". The LLM <strong>without</strong> RAG does not know the answer.</li>
-                 <li>Set "Retrieval Augmented Generation" to "True" and submit prompt "what is gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li>
+                 <li>Set "Retrieval Augmented Generation" to "<strong>False</strong>" and submit prompt "what is gpt-4". The LLM <strong>without</strong> RAG does not know the answer.</li>
+                 <li>Set "Retrieval Augmented Generation" to "<strong>True</strong>" and submit prompt "what is gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li>
                  <li>Experiment with different prompts, for example "what is gpt-4, answer in german" (arabic, chinese, hindi) or "write a poem about gpt-4".</li>
                  </ul>
                  In a production system, processing external data would be done in a batch process. An idea for a production system would be to perform LLM use cases on the