diff --git a/docs/v2/examples/openai.mdx b/docs/v2/examples/openai.mdx
index 3d66a777d..ca66e9a55 100755
--- a/docs/v2/examples/openai.mdx
+++ b/docs/v2/examples/openai.mdx
@@ -1,6 +1,6 @@
 ---
 title: 'OpenAI'
-description: "Load the dataset (ensure you're logged in with huggingface-cli if needed)"
+description: 'Load the dataset (ensure you're logged in with huggingface-cli if needed)'
 ---
 {/*  SOURCE_FILE: examples/openai/multi_tool_orchestration.ipynb  */}
 
@@ -322,7 +322,7 @@ Finally, the tool call and its output are appended to the conversation, and the
 
 ### Multi-tool orchestration flow
 
-Now let us try to modify the input query and the system instructions to the responses API in order to follow a tool calling sequence and generate the output.
+Now let us try to modify the input query and the system instructions to the responses API in order to follow a tool calling sequence and generate the output. 
 
 
 ```python
@@ -427,10 +427,10 @@ agentops.end_trace(tracer, end_state="Success")
 ```
 
 
-Here, we have seen  how to utilize OpenAI's Responses API to implement a Retrieval-Augmented Generation (RAG) approach with multi-tool calling capabilities. It showcases an example where the model selects the appropriate tool based on the input query: general questions may be handled by built-in tools such as web-search, while specific medical inquiries related to internal knowledge are addressed by retrieving context from a vector database (such as Pinecone) via function calls. Additonally, we have showcased how multiple tool calls can be sequentially combined to generate a final response based on our instructions provided to responses API. Happy coding!
+Here, we have seen  how to utilize OpenAI's Responses API to implement a Retrieval-Augmented Generation (RAG) approach with multi-tool calling capabilities. It showcases an example where the model selects the appropriate tool based on the input query: general questions may be handled by built-in tools such as web-search, while specific medical inquiries related to internal knowledge are addressed by retrieving context from a vector database (such as Pinecone) via function calls. Additonally, we have showcased how multiple tool calls can be sequentially combined to generate a final response based on our instructions provided to responses API. Happy coding! 
 
 
 <script type="module" src="/scripts/github_stars.js"></script>
 <script type="module" src="/scripts/scroll-img-fadein-animation.js"></script>
 <script type="module" src="/scripts/button_heartbeat_animation.js"></script>
-<script type="module" src="/scripts/adjust_api_dynamically.js"></script>
+<script type="module" src="/scripts/adjust_api_dynamically.js"></script>
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