Update app.py

app.py

@@ -68,9 +68,9 @@ 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 "<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 in one sentence, translate to german", "concerns about gpt-4", or "generate three haikus about gpt-4".</li>
+                 <li>Set "Retrieval Augmented Generation" to "<strong>False</strong>" and submit prompt "explain 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 "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 
                  <a href='https://www.youtube.com/playlist?list=PL2yQDdvlhXf_hIzmfHCdbcXj2hS52oP9r'>AWS re:Invent playlist</a>.\n\n
@@ -81,7 +81,7 @@ description = """<strong>Overview:</strong> The app demonstrates how to use a La
 
 gr.close_all()
 demo = gr.Interface(fn=invoke, 
-                    inputs = [gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1), gr.Radio([True, False], label="Retrieval Augmented Generation", value = False), gr.Textbox(label = "Prompt", value = "what is gpt-4", lines = 1)],
+                    inputs = [gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1), gr.Radio([True, False], label="Retrieval Augmented Generation", value = False), gr.Textbox(label = "Prompt", value = "explain gpt-4", lines = 1)],
                     outputs = [gr.Textbox(label = "Completion", lines = 1)],
                     title = "Generative AI - LLM & RAG",
                     description = description)