There are two primary ways to get answers from your data using LLMs today: have the model write SQL directly, or have it query through a structured ontology like the dbt Semantic Layer. Both work. Companies are getting real value from each. But they fail in very different ways, and understanding those failure modes is what actually matters when you're deciding which to use.

In 2023, we ran a benchmark comparing the two approaches and the Semantic Layer won handily. But 2023 is roughly 10 million years ago in LLM time. Models have gotten dramatically better at writing SQL. So we reran the benchmark with the latest generation models to see whether the gap has closed.

Introduction​

As a solutions architect at dbt Labs, my role is to help our customers and prospects understand how to best utilize the dbt Cloud platform to solve their unique data challenges. That uniqueness presents itself in different ways - organizational maturity, data stack, team size and composition, technical capability, use case, or some combination of those. With all those differences though, there has been one common thread throughout most of my engagements: Generative AI and Large Language Models (LLMs). Data teams are either 1) proactively thinking about applications for it in the context of their work or 2) being pushed to think about it by their stakeholders. It has become the elephant in every single (zoom) room I find myself in.