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Showing content from https://python.langchain.com/v0.1/docs/use_cases/extraction/guidelines/ below:

This is documentation for LangChain v0.1, which is no longer actively maintained. Check out the docs for the latest version here.

Guidelines

The quality of extraction results depends on many factors.

Here is a set of guidelines to help you squeeze out the best performance from your models:

Set the model temperature to 0.
Improve the prompt. The prompt should be precise and to the point.
Document the schema: Make sure the schema is documented to provide more information to the LLM.
Provide reference examples! Diverse examples can help, including examples where nothing should be extracted.
If you have a lot of examples, use a retriever to retrieve the most relevant examples.
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!
If the schema is very large, try breaking it into multiple smaller schemas, run separate extractions and merge the results.
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!
Add verification/correction steps (ask an LLM to correct or verify the results of the extraction).

Benchmark

Create and benchmark data for your use case using LangSmith 🦜️🛠️.
Is your LLM good enough? Use langchain-benchmarks 🦜💯 to test out your LLM using existing datasets.

Keep in mind! 😶‍🌫️

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.
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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