nits complete

Author: corwin-oai

examples/o-series/o3o4-mini_prompting_guide.ipynb

@@ -176,7 +176,7 @@
     "\n",
     "\n",
     "### Avoid Chain of Thought Prompting\n",
-    "Since these models are reasoning models and produce an internal chain of thought, they do not have to be explicitly prompted to plan and reason between toolcalls. Therefore, a developer should not try to induce additional reasoning before each function call by asking the model to plan more extensively. Asking a reasoning model to reason more may actually hurt the performance. \n",
+    "Since these models are reasoning models and produce an internal chain of thought, they do not have to be explicitly prompted to plan and reason between tool calls. Therefore, a developer should not try to induce additional reasoning before each function call by asking the model to plan more extensively. Asking a reasoning model to reason more may actually hurt the performance. \n",
     "\n",
     "A quick side note on reasoning summaries: the models will output reasoning tokens before calling tools. However, these will not always be accompanied by a summary, since our reasoning summaries require a minimum number of material reasoning tokens to produce a summary.\n"
    ]
@@ -188,7 +188,7 @@
     "# Responses API\n",
     "\n",
     "### Reasoning Items for Better Performance\n",
-    "We’ve released a [cookbook](https://cookbook.openai.com/examples/responses_api/reasoning_items) detailing the benefits of using the responses API. It is worth restating a few of the main points in this guide as well. o3/o4-mini are both trained with its internal reasoning persisted between toolcalls within a single turn. Persisting these reasoning items between toolcalls during inference will therefore lead to higher intelligence and performance in the form of better decision in when and how a tool gets called. Responses allow you to persist these reasoning items (maintained either by us or yourself through encrypted content if you do not want us to handle state-management) while Chat Completion doesn’t. Switching to the responses API and allowing the model access to reasoning items between function calls is the easiest way to squeeze out as much performance as possible for function calls. Here is an the example in the cookbook, reproduced for convenience, showing how you can pass back the reasoning item using `encrypted_content` in a way which we do not retain any state on our end:\n"
+    "We’ve released a [cookbook](https://cookbook.openai.com/examples/responses_api/reasoning_items) detailing the benefits of using the responses API. It is worth restating a few of the main points in this guide as well. o3/o4-mini are both trained with its internal reasoning persisted between tool calls within a single turn. Persisting these reasoning items between tool calls during inference will therefore lead to higher intelligence and performance in the form of better decision in when and how a tool gets called. Responses allow you to persist these reasoning items (maintained either by us or yourself through encrypted content if you do not want us to handle state-management) while Chat Completion doesn’t. Switching to the responses API and allowing the model access to reasoning items between function calls is the easiest way to squeeze out as much performance as possible for function calls. Here is an the example in the cookbook, reproduced for convenience, showing how you can pass back the reasoning item using `encrypted_content` in a way which we do not retain any state on our end:\n"
    ]
   },
   {