# Numbers Station's Approach to Foundation Models in the Modern Data Stack ## Company Overview Numbers Station is working on integrating foundation model capabilities into the modern data stack to accelerate time to insights. Their approach focuses on practical applications of LLMs in data processing and analysis workflows, addressing real-world challenges in enterprise environments. ## Foundation Model Applications ### SQL Generation - Implements natural language to SQL translation for business users - Reduces back-and-forth between business users and data engineering teams - Addresses challenges with complex queries requiring domain knowledge - Handles edge cases where multiple similar columns (e.g., date columns) exist ### Data Cleaning - Uses foundation models as an alternative to traditional rule-based cleaning - Leverages in-context learning with examples to derive cleaning patterns - Implements hybrid approaches combining rules with foundation models - Focuses on handling edge cases that traditional rules might miss ### Data Linkage - Addresses the challenge of linking records without common identifiers - Uses natural language prompts to determine record matches - Combines rule-based approaches with foundation models - Particularly useful for complex cases where simple rules fail ## Technical Challenges and Solutions ### Scale and Performance - Identified challenges with large model deployment: ### Solutions: - Implemented model distillation - Adopted hybrid approaches - Optimizes model usage based on task complexity ### Prompt Engineering Challenges - Observed issues with prompt brittleness - Documented significant performance variations based on: ### Solutions: - Developed prompt ensembling technique - Implemented prompt decomposition - Optimized demonstration sampling methods ### Domain Knowledge Integration ### Inference-time Solutions: - Augments foundation models with external memory - Integrates with: ### Training-time Solutions: - Continual pre-training on organization-specific data - Leverages internal documents, logs, and metadata - Develops domain-aware models through specialized training ## Integration with Modern Data Stack ### Tool Integration - Works with common data tools: ### Workflow Integration - Streamlines data processing pipeline - Automates manual data tasks - Reduces time to insights - Maintains compatibility with existing tools ## Best Practices and Recommendations ### Model Selection - Choose appropriate model sizes based on use case - Balance between accuracy and performance - Consider resource constraints ### Hybrid Approaches - Combine traditional rules with foundation models - Use rules for simple, high-volume cases - Apply foundation models for complex edge cases ### Quality Assurance - Implement robust testing for generated SQL - Validate data cleaning results - Verify data linkage accuracy - Monitor model performance in production ## Research and Development - Collaboration with Stanford AI Lab - Published research on prompt ensembling - Ongoing work in domain adaptation - Focus on practical enterprise applications ## Production Considerations ### Scalability - Addresses performance at enterprise scale - Implements efficient model serving strategies - Optimizes resource utilization ### Reliability - Ensures consistent model outputs - Handles edge cases gracefully - Maintains system stability ### Integration - Seamless connection with existing data tools - Minimal disruption to current workflows - Easy adoption for business users ## Future Directions ### Ongoing Development - Continued research in prompt optimization - Enhanced domain adaptation techniques - Improved model efficiency - Extended tool integration capabilities ### Expanding Applications - Exploring new use cases in data stack - Developing additional automation capabilities - Enhancing existing solutions based on feedback