Why LLMs Are Not (Yet) the Silver Bullet for Unstructured Data Processing (unstract.com)

Recent discussions in the AI/ML community have highlighted the limitations of Large Language Models (LLMs) in processing unstructured data. While LLMs hold promise for bridging the gap between structured and unstructured data, they currently face challenges such as high costs, slow processing speeds, and limited context windows, especially for organizations managing terabytes of data. The current infrastructure excels at handling structured data thanks to established technologies like SQL, but integrating unstructured data requires new strategies and tools that are still in the early stages of development. The potential for LLMs to serve as a powerful processing "CPU" is evident; however, the technology's nascent status means it is not yet the go-to solution for many unstructured data tasks. Effective extraction, transformation, and loading (ETL) processes are critical to leveraging LLMs for this purpose, especially in fields with high-volume, high-value unstructured data, such as legal and financial documents. The introduction of tools like Prompt Studio, designed for schema mapping, aims to streamline these processes, but navigating the complexities of various document formats remains a significant hurdle. As the ecosystem evolves, the synergy between LLMs and existing data stacks promises to enhance data processing capabilities, but users must consider both current efficiencies and future developments.