Data Values

The 'Included Elements' property allows you to control which parts of the Data Model hierarchy are serialized and provided to the AI as structured JSON. By default, if no elements are specified, all Data Elements in the relevant Data Model are included. When one or more elements are selected, only those elements and their descendants are present in the output, enabling focused or minimal data representations.

Configuration

• Use the property editor to select one or more Data Elements (such as fields, sections, tables, or models) from the available hierarchy.
• The selection supports hierarchical navigation, allowing you to include entire sections or tables by selecting their parent element.
• If the list is left empty, the entire Data Model is included in the output.

Impact of Changes

• Including specific elements: Only the selected Data Elements and their descendants will be present in the JSON output. This is useful for scenarios where only a subset of the document’s data is relevant to the AI workflow.
• Leaving the list empty: All data elements defined in the Data Model are included, providing a comprehensive export of the document’s extracted data.

Example

To include only the "Invoice Number" and "Total Amount" fields from an invoice Data Model, select those fields in the property editor. The resulting JSON will contain only the data for those fields and their child elements (if any).

{
  "Invoice Number": "INV-12345",
  "Total Amount":  "1,234.56"
}

Best Practices

• Use this property to limit the data sent to the AI to only what is necessary for your workflow, improving performance and reducing potential exposure of sensitive or irrelevant information.
• When working with large or complex Data Models, consider including only the elements required for the AI’s task to streamline processing and output.

For more information about Data Elements and their role in data extraction, see the documentation for Data Field, Data Section, Data Table, and Data Model.

Overview

The 'Name' property provides a concise, human-readable label for the quoting method. This value is used in Grooper’s user interface to help users identify, select, and configure quoting strategies. It is also injected into the prompt context sent to the large language model (LLM), where it serves as a section header or explicit label for the quoted content.

LLM Context Injection

• When the output of multiple quoting methods is combined (as in Multi Quote), the 'Name' is used to clearly distinguish each section of quoted content for the LLM. This helps the model understand that different representations or perspectives of the same document are being provided.
• In quoting methods such as Layout Objects, the 'Name' may be included in the prompt to clarify the structure or intent of the data, guiding the LLM’s interpretation and extraction logic.
• A descriptive and unambiguous name improves the LLM’s ability to reference, compare, and utilize the quoted data accurately, especially in complex or multi-view prompts.

Configuration and Usage

• Choose a name that reflects the quoting method’s purpose, formatting, or the type of content it produces.
• If left blank, the quoting method’s type name will be used as a default.
• Use consistent naming conventions to support clarity for both users and the LLM.

Example

• Claim Data
• Table Headers
• Layout Objects (JSON)

Best Practices

• Use names that are meaningful to both users and the LLM.
• When using Multi Quote, ensure each quoting method has a unique and descriptive name to maximize clarity in the combined prompt.
• Avoid generic names; be specific about the quoting method’s approach or output.

Overview

The 'Description' property provides a detailed, plain-language explanation of the content or data injected by this quoting method. It is intended to help the LLM understand the structure, semantics, and intended use of the quoted content, as well as to guide the model’s behavior when processing or extracting information from it.

LLM Context Injection

• The description is included directly in the prompt context sent to the LLM, typically as a prefix or annotation before the quoted data.
• In Multi Quote, each quoting method’s description is presented alongside its output, giving the LLM explicit instructions or context for interpreting each section.
• In Layout Objects, the description can clarify the meaning of structured layout data, JSON schemas, or other specialized formats, helping the LLM understand how to use the data for extraction or analysis.
• A well-crafted description can specify the format, highlight important features, explain relationships, or instruct the LLM on how to process the data (e.g., “This section contains a JSON array of line items, each with fields for description, quantity, and price. Extract each item as a separate record.”).

Configuration and Usage

• Write the description for the LLM, focusing on what the data is, how it is structured, and what the model should do with it.
• Include details about formatting, delimiters, field names, or any special conventions used in the quoted content.
• Use markdown formatting, examples, or sample data to illustrate the structure or intent.
• Update the description if the quoting method’s configuration or output changes.

Example

This section contains a table of invoice line items, formatted as plain text with tab-separated columns.
Each line represents a single item, with columns for description, quantity, and unit price.
Extract each line as a separate record and map the columns to their corresponding fields.

Best Practices

• Be explicit and unambiguous; avoid assumptions about the LLM’s prior knowledge.
• Tailor the description to the specific quoting method and output format.
• When using Multi Quote or Layout Objects, ensure each description is distinct and clearly describes the associated data.
• Use examples to illustrate complex or non-standard formats.