Getting AI to write good SQL: Text-to-SQL techniques explained

Great, this is a good looking query. But what happens when you move beyond this trivial example, and use Gemini for text-to-SQL against a real world database and on real-world user questions? It turns out that the problem is more difficult. The model needs to be complemented with methods to: provide business-specific context understand user intent manage differences in SQL dialects Let’s take a look at each of these challenges. Problem #1: Provide business-specific context Just like data analysts or engineers, LLMs need significant amounts of knowledge or "context" to generate accurate SQL. The context can be both explicit (what does the schema look like, what are the relevant columns, and what does the data itself look like?) or more implicit (what is the precise semantic meaning of a piece of data? what does it mean for the specific business case?). Specialized model training, or fine tuning, is typically not a scalable solution to this problem. Training on the shape of every da ... Read full article.