The paper presents Dubo-SQL, a new approach combining low-cost fine-tuning with advanced retrieval-augmented generation techniques to improve text-to-SQL translation performance on the BIRD-SQL benchmark. Dubo-SQL utilizes GPT-3.5 Turbo and GPT-4 Turbo variations, showing substantial strides in execution accuracy. Key highlights include:

• Establishment of new performance records on BIRD-SQL benchmarks.
• Achievements of lower costs and faster speeds compared to current methods.
• Innovative use of varied retrieval-augmented generation methods for better performance.

The significance of Dubo-SQL lies in its capability to significantly refine the precision of automated text-to-SQL transformation while minimizing operational costs, which could be particularly beneficial in resource-sensitive scenarios. Further research could focus on adapting these methods to other complex data parsing and transformation challenges.