With LLMs, the processing can be more dynamic. First, prompts and examples can steer LLMs toward the information extraction goals and help them work around document complexities. Second, the same LLMs can be used for ad hoc querying, and feedback mechanisms can be instrumented to improve the information extractions based on end-user prompts.
“The advancement of genAI and LLMs is allowing us to use natural language to describe a desired program, expression, or result, and they are particularly good at extracting data from unstructured and multimodal sources,” says Greg Benson, professor of computer science at the University of San Francisco and chief scientist at SnapLogic. “Accurate information extraction from documents, like PDFs, has been notoriously difficult to write as code. We are realizing the power of prompt engineering and how sharing a few examples of desired extracted data helps the LLM “learn” how to apply the pattern to future input documents.”
Integrate IDP for smarter workflows
IDP is a fan-in, fan-out process where documents are stored in multiple locations, and many downstream platforms, workflows, and analytics can leverage the extracted information. Enterprises with significant document repositories and many enterprise applications should consider iPaaS (integration platforms as a service), data fabrics, and data pipelines to manage the integrations.
