Update tutorials/how-to-implement-rag-generativeapis/index.mdx

Co-authored-by: SamyOubouaziz <[email protected]>

Author: bene2k1

tutorials/how-to-implement-rag-generativeapis/index.mdx

@@ -329,7 +329,7 @@ for r in custom_rag_chain.stream({"question":"your question", "context": context
     time.sleep(0.1)
 ```
 
-- Prompt template: The prompt template is meticulously crafted to direct the model's responses. It clearly instructs the model on how to leverage the provided context and emphasizes the importance of honesty in cases where it lacks information.
+- Prompt template: the prompt template is meticulously crafted to direct the model's responses. It clearly instructs the model on how to leverage the provided context and emphasizes the importance of honesty in cases where it lacks information.
 To make the responses more engaging, consider adding a light-hearted conclusion or a personalized touch. For example, you might modify the closing line to say, "Thank you for asking! I'm here to help with anything else you need!"
 Retrieving context:
 - The retriever.invoke(new_message) method fetches relevant information from your vector store based on the user’s query. It's essential that this step retrieves high-quality context to ensure that the model's responses are accurate and helpful.