RAGs to Riches?
The Realities of RAG and text-to-SQL

In the age of Generative AI and retrieval augmented generation (RAG) the spotlight is on enterprise data. The data can reside in operational systems, data lakes / data warehouses, or on a data management platform, and it is an essential piece required for GenAI app to provide much more personalized and accurate information. 

Large Language Models (LLMs) can access structured enterprise data via RAG and SQL statements. For all the undeniable advantages of using an LLM to generate SQL queries, there are also a number of issues that accompany this approach. 

 Join 2 industry leaders as they discuss: 

• What is retrieval augmented generation (RAG) and how does it work?

• The pros and cons of generating SQL with AI  

• Considerations for ensuring your enterprise data is GenAI-Ready  

• Closing the GenAI data gap