So because you got a good response the conclusion is invalid? How does the user know if they got a good response or a bad one? Due to the parameters passed most LLMs are functionally non-deterministic, rarely giving the same answer twice even with the same question.
I just asked chatgpt "whats the best database structure for a users table where you have users and admins?" in two different browser sessions. One gave me sql with varchars and a role column using:
role VARCHAR(20) NOT NULL CHECK (role IN ('user', 'admin')),
CREATE TYPE user_role AS ENUM ('user', 'admin', 'superadmin');
//other sql snipped
role user_role NOT NULL DEFAULT 'user',
So by extrapolation all of the IT books of the past were "wildly unhelpful" as no two of them presented the exact same solution to a problem, even all those pretending to be 'best practice'?
Your question is vague (technical reference, not meant derogatory). In which DBMS? By what metric of 'best'? For which size of database? Does it need to support internationalization? Will the roles be updated or extended in the future etc.
You could argue an AI Assistant would need to ask you this clarification if the question is vague rather than make a guess. But in extremis this is in practice not workable. If every minute factor needs to be answered by the user before getting a result, only the very experts would get to the stage of getting an answer if ever.
This is not just an AI problem, but a problem (human) business and technical analysts face every day in their work. When do you switch to proposing a solution rather than asking further details? It is BTW also why all those BPM or RPA platforms that promise to eliminate 'programming' and let the business analyst 'draw' a solution often fail miserably. They either have too narrow defaults or keep needing to be fed detail long past the BA's comfort zone.