4 Lessons for Generative AI from Snowflake's Text to SQL Model (Snowflake CoPilot).
To build generative AI models like the text-to-SQL system by Snowflake, it is important to create a realistic and challenging training dataset rather than relying on academic benchmarks that may be overly simplistic. Customized evaluation metrics that go beyond simple similarity scores but avoid the strictness of execution-based metrics, such as using language models for partial credit scoring, are valuable. Prompting strategies that provide relevant context like table metadata can improve performance. Finally, using a strong base model like Mistral Large can push the boundaries of what is achievable, allowing Snowflake's system to outperform even GPT-4 on the text-to-SQL task.
Read the series:
Part 1: All evaluation data sets are wrong, some are useful. / inside-snowflake-building-the-most-powerfu...
Part 2: Expanding Evaluation to be user-centric...with LLMs / inside-snowflake-building-the-most-powerfu...
Part 3: Retrieve, Prompt, Generate / inside-snowflake-building-the-most-powerfu...
Part 4. Mistral & Snowflake: The New Frontier in SQL Copilot Products
/ mistral-snowflake-the-new-frontier-in-sql-...
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