A developer evaluated Text-to-SQL and RAG for a company address database chatbot. The dataset, with 16 columns and 20,001 rows, required zero hallucinations and support for aggregate functions like averages and maximums. The developer chose Text-to-SQL for its deterministic accuracy and ability to handle these core operations directly on the structured data.

The decision hinged on the database's specific nature. Text-to-SQL efficiently handles frequent data changes, like column updates, without the computational overhead of re-indexing vector databases. With a planned limit of 100 concurrent users and horizontal scaling capabilities, this approach proved more efficient for the Proof of Concept, prioritizing precision over the broader contextual retrieval RAG offers.

For this use case, Text-to-SQL delivers the required performance and reliability. RAG excels when queries need unstructured data context or semantic search across documents. The choice ultimately depends on the data's structure and the specific functions the chatbot must perform. Developers should weigh deterministic accuracy against contextual flexibility when building similar AI-powered query systems.