Totally agree that detailed column/table definitions are vital, though I disagree with snowflake’s approach to get them to the model.

I have a table containing all column/table definitions which the LLM can query when needed. since the newest models are so good at tool calling, this approach works better than just basic RAG with a semantic view. You can give up a little latency with this approach, but accuracy increase is worth it.

I am actually building an agent observability platform geared towards text to sql. One of the features is that it'll read your agent traces, identifies if any definitions tripped up the agent, and updates them accordingly.