Enable Natural Language Prompting with AtScale’s Semantic Layer and Generative AI
As enterprises grow their data warehouses, the bottleneck of human analysts becomes more pronounced. Text-to-SQL solutions leveraging Large Language Models (LLMs) face challenges without a source of business logic and schema interactions. This whitepaper explores integrating the AtScale Semantic Layer and Query Engine with an LLM to improve Text-to-SQL performance.
Key Highlights
- Enhanced Accuracy: Achieves 92.5% accuracy in translating natural language questions into SQL queries.
- Simplified Query Generation: Removes the need for LLMs to generate joins or complex business logic, reducing errors and improving efficiency.
- Business Context Integration: Provides LLMs with essential business metadata, ensuring consistent and accurate results.
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About the Author
Jeff Curran is the Data Science Team Lead at AtScale, and has been with the team for over two years. Jeff has a degree in Physics from Northeastern University and a Masters of Business Intelligence and Data Analytics from Carnegie Mellon. Between his academic and professional experience, Jeff has been involved in the Data and Analytics space for over a decade.
