AI can generate SQL quickly, but it cannot rescue a chaotic data layer. If column names are vague, table purposes are undocumented, and definitions drift across teams, the model will generate plausible-looking queries against unstable meaning.
A human analyst can sometimes compensate for inconsistent names with context. A model cannot unless you provide it.
If tables and fields have ambiguous names, the model will still produce a query, but the confidence of the output will be much higher than the clarity of the underlying meaning.
Good naming is specific, stable, and descriptive enough that a new analyst can infer the purpose of the table without needing a side conversation.
Good documentation answers three questions: what the table contains, what the grain is, and what the most important caveats are.
The first payoff is speed. Teams spend less time deciphering the schema and more time asking better questions.
The second payoff is confidence. AI-generated SQL becomes easier to review because the query intent is clearer.