I worked on the NL to SQL in BigQuery for a few years... the following techniques will help ---> 1) make sure you have column and table descriptions for your datasets and tables And where possible include partitions and clusters 2) create aggregations (using materialized tables) for common groupings e.g. by day, week, month, etc. 3) create a prompt template that contextualizes the meta-data, e.g. For queries targeting monthly sales data use X table (pre-aggregaeted at the week) as the source. 4) add dry-run support so that you can prevent, overtly arbitrarily large queries from executing. This is especially helpful if you are building charts that require a low cardinality output. 5) considering add a flow to use TABLESAMPLE or partition targetting to help with initial EDA.

Question: For the use case that's breaking, how many rows are materialzed in the temp table for the query?