One huge implication of LLMs transforming data processing into semantic processing is the transformation of Extract-Transform-Load (ETL) data processes to Extract-Contextualize-Load (ECL) semantic processes.

LLMs as the form function for data interaction, using natural language to interact with unstructured natural language documents

An example of an ECL process would be the use of document hierarchies. In the document hierarchies process, data is extracted from the underlying documents and chunked, the data is contextualized into a hierarchical layer of metadata, and stored in a knowledge graph. The knowledge graph serves as a semantic layer for more accurate information retrieval before the information is ever loaded into the LLM.

The ELC process refers to some of the more innovative work going on with just-in-time and iterative knowledge graphs. The main use case here is in recursive retrieval, whereupon core concepts and ideas are fixed within a knowledge graph and information from various pages and documents are iteratively fed into the knowledge graph over time.

The transformation of ETL into ECL, powered by advancements in LLMs, marks a pivotal shift in data processing from a focus on structured data integration to engaging with unstructured data through semantic understanding.