Panzura's Glen Shok introduces metadata harvesting and its vital role in bringing structure to unstructured data, so LLMs can easily access and understand it.
We're swimming in a sea of unstructured, file data. From sprawling file servers to cloud-based storage, 80% of our data exists in this chaotic, yet potentially invaluable, form. But how do we extract meaningful insights and leverage this data to train LLMs and other AI models effectively?
The answer lies in metadata harvesting.
Unstructured data is a dilemma for the LLM. It's scattered, inconsistent, and often inaccessible. When it comes to training AI models, garbage in equals garbage out. Without a clear structure, LLMs struggle to retrieve relevant information, leading to poor performance and inaccurate responses.
Metadata harvesting brings order to the chaos. It's the process of extracting key information (metadata) from your unstructured data and organizing it into a structured, searchable metadata catalog. Think of it as creating a detailed index for your massive library that lets AI models find and understand the information they need.