Update app.py

app.py

@@ -67,11 +67,11 @@ description = """<strong>Overview:</strong> The app demonstrates how to use a La
                  (in this case a YouTube video, but it could be PDFs, URLs, or other structured/unstructured private/public 
                  <a href='https://raw.githubusercontent.com/bstraehle/ai-ml-dl/c38b224c196fc984aab6b6cc6bdc666f8f4fbcff/langchain/document-loaders.png'>data sources</a>).\n\n
                  <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 about GPT-4</a>.
+                 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>Experiment with different prompts, for example "what is gpt-4, answer in german" or "write a poem about gpt-4".</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 
                  <a href='https://www.youtube.com/playlist?list=PL2yQDdvlhXf_hIzmfHCdbcXj2hS52oP9r'>AWS re:Invent playlist</a>.\n\n