diff --git a/examples/File_Search_Responses.ipynb b/examples/File_Search_Responses.ipynb
index 16dadb7..aaa49f4 100644
--- a/examples/File_Search_Responses.ipynb
+++ b/examples/File_Search_Responses.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# Using file search tool in the Responses API\n",
     "\n",
-    "RAG can be overwhelming, searching amongst PDF file shouldn't be complicated. One of the most adopted options as of now is parsing your PDF, defining your chunking strategies, uploading those chunks to a storage provider, running embeddings on those chunks of texts and storing those embeddings in a vector database. That's only for the set-up, we're not yet at the step retrieving content in our LLM workflow which would also require multiple steps.\n",
+    "Although RAG can be overwhelming, searching amongst PDF file shouldn't be complicated. One of the most adopted options as of now is parsing your PDF, defining your chunking strategies, uploading those chunks to a storage provider, running embeddings on those chunks of texts and storing those embeddings in a vector database. And that's only the setup — retrieving content in our LLM workflow also requires multiple steps.\n",
     "\n",
     "This is where file search — a hosted tool you can use in the Responses API — comes in. It allows you to search your knowledge base and generate an answer based on the retrieved content. In this cookbook, we'll upload those PDFs to a vector store on OpenAI and use file search to fetch additional context from this vector store to answer the questions we generated in the first step. Then, we'll initially create a small set of questions based on PDFs extracted from OpenAI's blog ([openai.com/news](https://openai.com/news)).\n",
     "\n",
-    "_File search was previously available on the Assistants API, it is now available on a stateless API that is Responses and benefits from new features (e.g: metadata filtering)_\n",
+    "_File search was previously available on the Assistants API. It's now available on the new Responses API, an API that can be stateful or stateless, and with from new features like metadata filtering_\n",
     "\n",
     "### Set up"
    ]
@@ -660,7 +660,7 @@
     "- Understand how chunks of texts are retrieved, ranked and used as part of the Response API\n",
     "- Measure accuracy, precision, retrieval, MRR and MAP on the dataset of evaluations previously generated\n",
     "\n",
-    "By using file search with Responses, you can simplify RAG architecture and leverage this in a single API call using the new Responses API. File storage, embeddings, & retrieval all integrated in one tool!\n"
+    "By using file search with Responses, you can simplify RAG architecture and leverage this in a single API call using the new Responses API. File storage, embeddings, retrieval all integrated in one tool!\n"
    ]
   }
  ],