LLM-based methods

Extract free text from unstructured documents using large language model (LLM)-based SenseML methods. For example, extract information from legal paragraphs in contracts and leases, or results from research papers.

These methods are low-code alternatives to layout-based methods for structured documents, for example, tax documents or insurance forms.

The following topics describe how to author LLM-based methods using the SenseML editor. For information about authoring LLM-based methods using a visual tool instead of JSON, see Prompt tips.

Method	Example use case	Notes
List method	”For each vehicle in an auto insurance declaration, extract the VIN, model, and year.”	Extracts a list of data out of a document, where you don’t know how the data are represented.
NLP Table method	”For each transaction in a bank statement table, extract the date and amount.”	Extracts a list of data out of a document, where you know they’re in a table.
Query Group method	”When does the policy period end?""What are the last 4 numbers of the account?”	Extracts a single fact or data point.
Summarizer computed field method	transform extracted data using LLM prompts	Use this method to transform another method’s output when you can’t use types or other computed field methods.

Notes

For layout-based extraction methods, see methods.