Docs: Revamp Extraction Use Case

docs/docs/use_cases/extraction/guidelines.ipynb

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+    "---\n",
+    "title: Guidelines\n",
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+    "---"
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+    "The quality of extraction results depends on many factors. \n",
+    "\n",
+    "Here is a set of guidelines to help you squeeze out the best performance from your models:\n",
+    "\n",
+    "* Set the model temperature to `0`.\n",
+    "* Improve the prompt. The prompt should be precise and to the point.\n",
+    "* Document the schema: Make sure the schema is documented to provide more information to the LLM.\n",
+    "* Provide reference examples! Diverse examples can help, including examples where nothing should be extracted.\n",
+    "* If you have a lot of examples, use a retriever to retrieve the most relevant examples.\n",
+    "* Benchmark with the best available LLM/Chat Model (e.g., gpt-4, claude-3, etc) -- check with the model provider which one is the latest and greatest!\n",
+    "* If the schema is very large, try breaking it into multiple smaller schemas, run separate extractions and merge the results.\n",
+    "* Make sure that the schema allows the model to REJECT extracting information. If it doesn't, the model will be forced to make up information!\n",
+    "* Add verification/correction steps (ask an LLM to correct or verify the results of the extraction).\n",
+    "\n",
+    "## Benchmark\n",
+    "\n",
+    "* Create and benchmark data for your use case using [LangSmith 🦜️🛠️](https://docs.smith.langchain.com/).\n",
+    "* Is your LLM good enough? Use [langchain-benchmarks 🦜💯 ](https://github.com/langchain-ai/langchain-benchmarks) to test out your LLM using existing datasets.\n",
+    "\n",
+    "## Keep in mind! 😶‍🌫️\n",
+    "\n",
+    "* LLMs are great, but are not required for all cases! If you’re extracting information from a single structured source (e.g., linkedin), using an LLM is not a good idea – traditional web-scraping will be much cheaper and reliable.\n",
+    "\n",
+    "* **human in the loop** If you need **perfect quality**, you'll likely need to plan on having a human in the loop -- even the best LLMs will make mistakes when dealing with complex extraction tasks."
+   ]
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docs/docs/use_cases/extraction/how_to/_category_.yml

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+label: 'How-To Guides'
+position: 1