The retrieval-augmented generation approach in Dubo-SQL v2 offers several benefits but also comes with potential drawbacks compared to the fine-tuning approach of Dubo-SQL v1:
Inference Cost: Retrieval-augmented generation may incur higher inference costs due to the need for more tokens and a larger model like GPT-4 Turbo, making it more expensive compared to fine-tuning with a smaller model like GPT-3.5 Turbo.
Complexity: The process of selecting diverse few-shot examples and managing the conversation history to aid the model's learning can introduce complexity and potential challenges in implementation and maintenance.
Model Performance: While retrieval-augmented generation can provide a broader context for understanding complex queries, it may not always outperform fine-tuned models in terms of execution accuracy, especially in scenarios where fine-tuning can provide more tailored solutions.
Scalability: Scaling the retrieval-augmented generation approach to handle extremely large databases with thousands of tables and columns may pose challenges in maintaining efficiency and effectiveness compared to fine-tuning.
Training Data Requirements: Retrieval-augmented generation relies on diverse few-shot examples, which may require careful curation and management of training data to ensure optimal performance, adding to the complexity of the approach.