What's the Minimum Viable Context for Building a Canonical Data Model with an LLM?

In discussing the challenges of building a Canonical Data Model (CDM) with a Large Language Model (LLM), the text explores three approaches to determining the minimum viable context necessary for effective modeling. The first approach, the "20 Questions Method," involves a guided Q&A to extract key concepts, but often results in overwhelming the LLM with too much information, leading to unnecessary complexity. The second approach uses business scenarios to define relationships, but can struggle with scope issues when scenarios cross departmental boundaries, causing the LLM to unnecessarily link unrelated entities. The third approach focuses on starting with intent, allowing users to specify a development goal, which streamlines the process by providing a focused ontology, although it may not accommodate highly specific use cases. Overall, the takeaway emphasizes the importance of providing clear, focused input to the LLM to yield a useful data model, while also highlighting the tools available in the dltHub AI Workbench for enhancing data pipeline development.