WHITEPAPER

Enable Natural Language Prompting with AtScale’s Semantic Layer & Generative AI

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Discover how integrating AtScale’s Semantic Layer with Generative AI enhances Text-to-SQL performance, achieving 92.5% accuracy and simplifying query generation by providing essential business metadata for consistent, precise results.

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.

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