The bottom line here is that a new generation of foundation models is giving enterprises much more finely tuned control over how they leverage generative AI. They no longer must settle for generic models that were not designed for their particular use cases. They can instead customize model behavior in extensive ways – provided they have the data engineering capabilities to do so.
Historically, GenAI models trained primarily on netherlands whatsapp number data unstructured data – such as documents and Web pages – because the primary goal of the models’ designers was to allow users to search or summarize data within those documents. Essentially, GenAI models like those developed by OpenAI are alternative search interfaces for the Web.
This remains one important use case for GenAI within business. An additional, emerging use case is to leverage GenAI as an interface for querying structured data – such as information stored in databases – as well. Enterprises can already do this using solutions like Amazon Q.
This is significant because it indicates that GenAI can enhance businesses’ ability to interpret the vast volumes of structured data they possess. In the past, addressing questions based on this data required expert data analysts who wrote complex queries by hand and then generated reports. Now, GenAI can do that work at a rate much faster than even the most skilled data analyst could achieve.