Update app.py

app.py

@@ -72,7 +72,7 @@ description = """<strong>Overview:</strong> The app demonstrates how to use a La
                  <li>Set "Retrieval Augmented Generation" to "<strong>True</strong>" and submit prompt "explain gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li>
                  <li>Experiment with different prompts, for example "explain gpt-4 in one sentence in german", "list pros and cons of gpt-4", or "generate a haiku about gpt-4".</li>
                  </ul>
-                 In a production system, managing external data would be done in a batch process. An idea for a production system would be to perform LLM use cases on the 
+                 In a production system, embedding external data would be done in a batch process. An idea for a production system would be to perform LLM use cases on the 
                  <a href='https://www.youtube.com/playlist?list=PL2yQDdvlhXf_hIzmfHCdbcXj2hS52oP9r'>AWS re:Invent playlist</a>.\n\n
                  <strong>Technology:</strong> <a href='https://www.gradio.app/'>Gradio</a> UI using <a href='https://platform.openai.com/'>OpenAI</a> API via AI-first 
                  <a href='https://www.langchain.com/'>LangChain</a> toolkit with <a href='https://openai.com/research/whisper'>Whisper</a> (speech-to-text) and