When we first heard about AI-powered SQL assistants, we were skeptical: is it really possible for a business analyst or a developer without deep SQL expertise to easily create complex queries? To find out, our colleague Márton Schmalzl conducted a proof of concept (POC) test, and now we’re sharing his experiences.
The Goal of the POC
Our aim was to test an AI-based SQL assistant in a real-world environment. We were looking for a tool that could generate SQL queries based on natural language instructions, optimize them, and—if needed—explain them. Our expectation was that the assistant would significantly reduce the time spent writing SQL queries and help non-expert users navigate databases more efficiently.
The Test Environment
For the POC, we used an anonymized business database containing a large volume of transactional and customer data. The AI assistant was tasked with generating queries that analyzed, for example, customer group buying behavior, financial performance, or data quality issues.
The tested AI assistant was built on a well-known advanced language model and was integrated into an SQL editor interface. Users could input their questions in natural language, and the AI would generate and even execute the corresponding SQL commands.
Key Takeaways
Near-perfect for simple queries
In our first tests, we asked basic questions such as “Which customer spent the most last year?” or “What was the average basket value by month?” The AI generated the correct SQL code without issue, and the responses were accurate.
Challenges with complex queries
When we tried more advanced use cases—joining multiple tables, comparing time periods, or embedding business logic (e.g., segmenting returning customers)—the AI didn’t always get it right on the first try. In some cases, when the query required multiple joins, the assistant “hallucinated” columns that didn’t exist in the base tables. These situations required manual fine-tuning, often done by a colleague with stronger SQL skills.
Explanations and error handling
One of the most pleasant surprises was that the AI didn’t just generate queries—it also provided feedback. When a query was incorrect, the assistant attempted to explain what went wrong and suggested possible fixes. This proved to be a huge help for less experienced users.
Performance optimization: not quite there yet
The generated SQL code occasionally included redundant elements or wasn’t fully optimized for execution on large databases. This was especially apparent in the handling of indexes and efficient JOINs. Without a DBA or experienced developer, achieving optimal query performance wasn’t always possible.
Summary: Can Anyone Become a Database Expert?
The POC made it clear that AI-based SQL assistants are incredibly useful tools—especially for those who don’t write SQL on a daily basis. For simple and moderately complex queries, they can save significant time and help users analyze data more quickly.
However, another key insight was that the AI assistant is not a mind reader. It works best when the database structure and column names are clear and descriptive so that the intent of the natural language question can be easily inferred. Otherwise, it may misinterpret the query or fail to retrieve the correct data.
We’re not yet at the point where a SaaS-based, plug-and-play solution can fully replace SQL knowledge. For more complex business logic or performance-critical systems, customization and environment-specific optimization are still essential. Once that’s done, however, the assistant can become a powerful tool—providing up-to-date insights based on plain-language questions or even generating custom reports without development effort.
So ultimately, the question isn’t whether AI assistants can turn anyone into a database expert, but rather: how much SQL knowledge is really needed for everyday work? For those who only occasionally work with data or do so from the business side, these tools are already a tremendous help—and we’re only beginning to tap into their full potential.
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