u/Ok-Working3200

I have been working on building a semantic layer using dbt in Snowflake. The biggest issue I have had is dealing with small ambiguity between business measures. I have built a testing agent in our ci/cd process to grade the semantic layer.

It just hit me today like a brick that er should train users, not just engineering users to use Plan mode with LLMs. When I turned plan mode on, any possible ambiguity came up in the plan and Claude asked me to clarify. I just wanted to share because I don't think using plan mode for business users is obvious.

Is there a continence within a couple of kilometers near Eurostars Grand Marina where I can buy eye glass cleaner before midnight?

My team is testing the Snowflake mcp self-hosted via Claude. We are using the official documentation from Snowflake labs. Has anyone noticed getting results by using the query manager over cortex analyst.

We have about six semantic views. Each view is listed with its own service name and description under analyst service in the config yaml. When we remove that samectiob and just leave query manager all of our verified queries, Ai sql generation and comments are being respected..

Our instructions are using words like Dont' and we describe when to use something.

The obvious problem i see is we have messy data, but with good descriptions. For example, If I say give me a count of active members, I want Claude to use a model that isn't called subscriptions. It feels like Cortex grabs the most lovey information quickly and decides, where query manager grabs all the Metadata first and then makes a decision.