docs: structured_output

docs/docs/guides/structured_output.ipynb

@@ -7,20 +7,20 @@
    "source": [
     "# [beta] Structured Output\n",
     "\n",
-    "It is often crucial to have LLMs return structured output. This is because often times the outputs of the LLMs are used in downstream applications, where specific arguments are required. Having the LLM return structured output reliably is necessary for that.\n",
+    "It is often crucial to have LLMs return structured output. This is because oftentimes the outputs of the LLMs are used in downstream applications, where specific arguments are required. Having the LLM return structured output reliably is necessary for that.\n",
     "\n",
     "There are a few different high level strategies that are used to do this:\n",
     "\n",
-    "- Prompting: This is when you ask the LLM (very nicely) to return output in the desired format (JSON, XML). This is nice because works with all LLMs, this is not nice because it doesn't garuntee that the LLM returns in the right format.\n",
-    "- Function calling: This is when the LLM is finetuned to be able to not just generate a completion, but also generate a function call. The functions the LLM can call are generally passed as extra parameters to the model API. The function names and descriptions should be treated as part of the prompt (they usually count against token counts, and are used by the LLM to decide what to do).\n",
+    "- Prompting: This is when you ask the LLM (very nicely) to return output in the desired format (JSON, XML). This is nice because it works with all LLMs. It is not nice because there is no guarantee that the LLM returns the output in the right format.\n",
+    "- Function calling: This is when the LLM is fine-tuned to be able to not just generate a completion, but also generate a function call. The functions the LLM can call are generally passed as extra parameters to the model API. The function names and descriptions should be treated as part of the prompt (they usually count against token counts, and are used by the LLM to decide what to do).\n",
     "- Tool calling: A technique similar to function calling, but it allows the LLM to call multiple functions at the same time.\n",
-    "- JSON mode: This is when the LLM is garunteed to return JSON.\n",
+    "- JSON mode: This is when the LLM is guaranteed to return JSON.\n",
     "\n",
     "\n",
     "\n",
     "Different models may support different variants of these, with slightly different parameters. In order to make it easy to get LLMs to return structured output, we have added a common interface to LangChain models: `.with_structured_output`. \n",
     "\n",
-    "By invoking this method (and passing in a JSON schema or a Pydantic model) the model will add whatever model parameters + output parsers are necessary to get back the structured output. There may be more than one way to do this (eg function calling vs JSON mode) - you can configure which method to use by passing into that method.\n",
+    "By invoking this method (and passing in a JSON schema or a Pydantic model) the model will add whatever model parameters + output parsers are necessary to get back the structured output. There may be more than one way to do this (e.g., function calling vs JSON mode) - you can configure which method to use by passing into that method.\n",
     "\n",
     "Let's look at some examples of this in action!\n",
     "\n",