Use ChatGPT Work to ground analysis in your semantic layer
Summary
ChatGPT can reliably query complex data when grounded in a semantic layer—a centralized knowledge hub containing entities, metrics, filters, and domain expertise. Organizations must invest focused effort to build and maintain this layer, but can leverage existing investments in Snowflake Cortex or Databricks Genie rather than starting from scratch.
Key Takeaways
- Create a semantic layer plugin containing key entities, metrics, standard filters, dimensions, and known pitfalls to make data knowledge accessible to AI agents for accurate query execution.
- Have domain experts from your data team review and refine the semantic layer before packaging it into a plugin—this human validation step is critical for agent decision-making accuracy.
- Start with well-contained domains to test semantic layer implementation; this reduces complexity and increases success probability for initial deployments.
- Integrate existing semantic layer investments (Snowflake Cortex, Databricks Genie) rather than building new—reuse what your organization already has to accelerate deployment.
- Treat semantic layer maintenance as an ongoing organizational priority requiring sustained focus and dedicated team resources, not a one-time implementation project.
Related topics
Transcript Excerpt
I'm sure many of you in the audience are probably wondering how we would even trust chat GPT to find and query the correct data. Then even simple data questions can have very complex queries or metric definitions underpinning them and that knowledge can be scattered across tools, across people and if it's not accessible to the agent then it's not going to be able to make the right decision. So it's all about making it accessible to the agent. And with the data plugin we can take that existing knowledge and then turn it into a shared skill. So this this site semantic layer it contains information about key entities, key metrics, different standard filters or dimensions. It has some information on like open questions, pitfalls. Someone from your data team who is or who is familiar with this …