diff --git a/examples/reasoning_function_calls.ipynb b/examples/reasoning_function_calls.ipynb
index 58b45ff..2c26478 100644
--- a/examples/reasoning_function_calls.ipynb
+++ b/examples/reasoning_function_calls.ipynb
@@ -8,11 +8,11 @@
     "OpenAI now offers function calling using [reasoning models](https://platform.openai.com/docs/guides/reasoning?api-mode=responses). Reasoning models are trained to follow logical chains of thought, making them better suited for complex or multi-step tasks.\n",
     "> _Reasoning models like o3 and o4-mini are LLMs trained with reinforcement learning to perform reasoning. Reasoning models think before they answer, producing a long internal chain of thought before responding to the user. Reasoning models excel in complex problem solving, coding, scientific reasoning, and multi-step planning for agentic workflows. They're also the best models for Codex CLI, our lightweight coding agent._\n",
     "\n",
-    "For the most part, using these models via the API is very simple and comparable to using familiar classic 'chat' models. \n",
+    "For the most part, using these models via the API is very simple and comparable to using familiar 'chat' models. \n",
     "\n",
     "However, there are some nuances to bear in mind, particularly when it comes to using features such as function calling. \n",
     "\n",
-    "All examples in this notebook use the newer [Responses API](https://community.openai.com/t/introducing-the-responses-api/1140929) which provides convenient abstractions for managing conversation state. The principles here are however relevant when using the older chat completions API."
+    "All examples in this notebook use the newer [Responses API](https://community.openai.com/t/introducing-the-responses-api/1140929) which provides convenient abstractions for managing conversation state. However the principles here are relevant when using the older chat completions API."
    ]
   },
   {
@@ -86,9 +86,10 @@
    "source": [
     "Nice and easy!\n",
     "\n",
-    "We're asking relatively complex questions that may requires the model to reason out a plan and proceed through it in steps, but this reasoning is hidden from us. We simply wait a little longer before being shown the output. \n",
+    "We're asking relatively complex questions that may require the model to reason out a plan and proceed through it in steps, but this reasoning is hidden from us - we simply wait a little longer before being shown the response. \n",
+    "\n",
     "However, if we inspect the output we can see that the model has made use of a hidden set of 'reasoning' tokens that were included in the model context window, but not exposed to us as end users.\n",
-    "We can see these tokens and a summary of the reasoning (but not the literal tokens used) in the response"
+    "We can see these tokens and a summary of the reasoning (but not the literal tokens used) in the response."
    ]
   },
   {
@@ -129,7 +130,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "It is important to know about these reasoning tokens, because it means we will consume our available context window more quickly than with traditional chat models. More on this later.\n",
+    "It is important to know about these reasoning tokens, because it means we will consume our available context window more quickly than with traditional chat models.\n",
     "\n",
     "## Calling custom functions\n",
     "What happens if we ask the model a complex request that also requires the use of custom tools?\n",
@@ -182,7 +183,10 @@
     "# Let's add this to our defaults so we don't have to pass it every time\n",
     "MODEL_DEFAULTS[\"tools\"] = tools\n",
     "\n",
-    "response = client.responses.create(input=\"What's the internal ID for the lowest-temperature city?\", previous_response_id=response.id, **MODEL_DEFAULTS)\n",
+    "response = client.responses.create(\n",
+    "    input=\"What's the internal ID for the lowest-temperature city?\",\n",
+    "    previous_response_id=response.id,\n",
+    "    **MODEL_DEFAULTS)\n",
     "print(response.output_text)\n"
    ]
   },
@@ -219,7 +223,8 @@
    "metadata": {},
    "source": [
     "Along with the reasoning step, the model has successfully identified the need for a tool call and passed back instructions to send to our function call. \n",
-    "Let's invoke the function and pass the results back to the model so it can continue.\n",
+    "\n",
+    "Let's invoke the function and send the results to the model so it can continue reasoning.\n",
     "Function responses are a special kind of message, so we need to structure our next message as a special kind of input:\n",
     "```json\n",
     "{\n",
@@ -410,7 +415,7 @@
     "* We may wish to store messages in our own database for audit purposes rather than relying on OpenAI's storage and orchestration\n",
     "* etc.\n",
     "\n",
-    "In these situations we will treat the API as stateless - rather than using `previous_message_id` we will instead make and maintain an array of conversation items that we add to and pass as input. This allows us full control of the conversation.\n",
+    "In these situations we may wish to take full control of the conversation. Rather than using `previous_message_id` we can instead treat the API as 'stateless' and make and maintain an array of conversation items that we send to the model as input each time.\n",
     "\n",
     "This poses some Reasoning model specific nuances to consider. \n",
     "* In particular, it is essential that we preserve any reasoning and function call responses in our conversation history.\n",