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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).


Keep in mind! πŸ˜Άβ€πŸŒ«οΈβ€‹

  • 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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