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I often do complicated reports in SQL or complicated transformations in SQL for system migrations. To really write a query and 'get it right' you usually need i

I often do complicated reports in SQL or complicated transformations in SQL for system migrations. To really write a query and 'get it right' you usually need insider knowledge that you can't glean from the column names. I see in their training set they've got comments about columns too. e.g. "Country" TEXT NOT NULL, - country where the singer born But thats still not enough. You also need a bunch of information about the real business that the data is describing. And you also need to analyse the whole database - is that field actually used? What are the common values for this picklist? What does that status actually mean in terms of business? If there are two of those rows that match the join but I want to avoid duplicates, which one do I take? - the newest, or the one with a certain status? etc.

capableweb 3y ago
Why dont you collect a sample of the data so it knows what to expect then? Include 10 handpicked rows in your prompt and it should be able to pick up the patterns. If you find it doesn't handle something, append that row and restart.
ainesh93 3y ago
> You also need a bunch of information about the real business that the data is describing. While the article focuses on finetuning GPT-3.5-turbo, how you use the text-to-SQL engine within the architecture of your overall solution is for you to decide. Providing this business context from vectorized context stores in the actual prompt would be a step in the right direction.
codeulike 3y ago
Also, with complicated queries or reports, there's quite a big element of "do the results look roughly as I expect?" - e.g does the total income for last month look about right? Then you might do a detailed reconciliation and find out your total is 1% off due to not accounting for refunds and then you go and adjust your query and try again.

capableweb 3y ago

Why dont you collect a sample of the data so it knows what to expect then? Include 10 handpicked rows in your prompt and it should be able to pick up the patterns. If you find it doesn't handle something, append that row and restart.

ainesh93 3y ago

> You also need a bunch of information about the real business that the data is describing. While the article focuses on finetuning GPT-3.5-turbo, how you use the text-to-SQL engine within the architecture of your overall solution is for you to decide. Providing this business context from vectorized context stores in the actual prompt would be a step in the right direction.

codeulike 3y ago

Also, with complicated queries or reports, there's quite a big element of "do the results look roughly as I expect?" - e.g does the total income for last month look about right? Then you might do a detailed reconciliation and find out your total is 1% off due to not accounting for refunds and then you go and adjust your query and try again.