AI SQL Accuracy: Testing different LLMs + context strategies to maximize SQL generation accuracy

The article discusses the use of autonomous AI agents to answer business queries in plain English, focusing on the use of Language Learning Models (LLMs) to generate SQL. The authors found that the accuracy of the LLMs increased significantly from around 3% to 80% when provided with the right context, such as schema definitions, documentation, and prior SQL queries. They tested different LLMs, including Google Bison, GPT 3.5, GPT 4, and Llama 2, and found that GPT 4 was the best overall for generating SQL, with Google’s Bison performing similarly when given enough context.

The authors also outlined their plans for future research, including testing on other datasets and databases, adding more training data, and experimenting with more foundational models. They are developing a Python package that can generate SQL for specific databases, generate Plotly code for charts, and generate follow-up questions. The package, called Vanna, can be trained using schema, documentation, and SQL examples.

Key takeaways:

The study shows that context is crucial in achieving accuracy in AI-generated SQL, with the right context improving accuracy from around 3% to 80%.
The research compared different Language Learning Models (LLMs) including Google Bison, GPT 3.5, GPT 4, and Llama 2, with GPT 4 emerging as the best overall LLM for generating SQL.
The study also demonstrated how to use the methods to generate SQL for your database, with a Python package in development to generate SQL for specific databases.
Future steps to improve accuracy include using other datasets, adding more training data, trying more databases, and experimenting with more foundational models.

AI SQL Accuracy: Testing different LLMs + context strategies to maximize SQL generation accuracy

Key takeaways:

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