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This feature allows you to automatically extract and summarize information from documents and output the results into a structured table. The AI model analyzes each document in the dataset and generate a structured row in a table based on your custom-defined fields and instructions. These fields can represent content summaries, metadata of interest, and more. For each instruction:
  • You define what the AI should extract or summarize
  • You configure a set of fields, each with:
    • Field Name
    • Field Type
    • Field Description (used as guidance to the model)
    • Whether the field is a list of values or a single value
The AI model uses your instructions and field descriptions as prompts to generate values for each document.

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How to Use

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1. Create an Instruction

Each instruction represents a task description for table generation. Example:
Summarize each document for identifying key topics and legal issues discussed.
You can also set a priority for this instruction to control execution order if multiple instructions are defined.

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2. Define Fields

For each instruction, define fields that should be populated in the output table. Each field includes:

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Output

Once configured, the system will:
  1. Go through every document in the dataset.
  2. Use the field descriptions as natural language prompts to generate content for each field.
  3. Output the result as a structured table, with one row per document and columns for each defined field.

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Notes & Best Practices

  • Use clear, descriptive language in field descriptions – this acts as the prompt for the model.
  • Use “Is List” for fields like recipients to ensure multiple values are correctly extracted.
  • Use multiple fields to break up different aspects of the summary (e.g., “Summary” vs. “Legal Issue” vs. “Entities”)
  • Limit the total number of fields to be fewer than 50 per model.
  • Currently the model is limited to generate 4K tokens (~2.8K words) per model.