Enhancing the comments on the existing data model seems to be the most common approach for sure. I'm implementing this as a data architecture at several clients and I've found creating a whole new logical structure designed for the LLM is really effective. Not being bound by the original data model lets you solve several problems related to the "n-hops" question, avoiding needing the comments, and the semantics of how data engineers define columns. Some more details here [1], but obviously you can implement this totally yourself by hand.
Comments
Enhancing the comments on the existing data model seems to be the most common approach for sure. I'm implementing this as a data architecture at several clients and I've found creating a whole new logical structure designed for the LLM is really effective. Not being bound by the original data model lets you solve several problems related to the "n-hops" question, avoiding needing the comments, and the semantics of how data engineers define columns. Some more details here [1], but obviously you can implement this totally yourself by hand.
[1] (https://github.com/eloquentanalytics/pyeloquent/blob/main/RE...)