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.
The Paper Details
Explore the need for efficient data analysis as businesses expand their data warehouses and the potential of integrating LLMs with AtScale’s technology. Learn about AtScale’s core components, our experimental methodology, and the impressive results demonstrating the impact on LLM performance. Discover the benefits and future potential of integrating AtScale’s Semantic Layer and Query Engine with LLMs for Text-to-SQL tasks.
Announcing the Open-Source of our NLQ Benchmarks:
AtScale has launched a public leaderboard to provide an objective benchmark for evaluating Text-to-SQL solutions, addressing the inconsistency in existing evaluation methods. By utilizing a transparent scoring system based on the TPC-DS dataset, AtScale’s benchmark allows solutions to be assessed across standardized question and schema complexity metrics. This initiative aims to foster collaboration and transparency in the industry, enabling companies to effectively compare Text-to-SQL models and advance natural language querying in data analytics.
If you have a solution you think should be included on the leaderboard or suggestions on how to improve the benchmark, we encourage you to email ailink@atscale.com.
Want to Learn More? Check out these articles:
- How Semantic Layers Make GenAI 3X More Accurate Than Direct SQL
- The Business Impact of Using a Semantic Layer for AI and BI
- AtScale Unveils Natural Language Query Capabilities, Revolutionizing Data Access for Business Users
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.
