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Text-to-SQL with GenAI & LLMs

Built enterprise Text-to-SQL solutions using Generative AI/LLMs fine-tuned on domain-specific schemas. Improved query accuracy by 30-40% and enabled self-service analytics across business teams, transforming natural language into complex SQL queries.

Bain & Company
2024
Empowered non-technical users with self-service analytics capabilities

Project Overview

This innovative solution transformed how business analysts and stakeholders interact with complex databases. By leveraging Generative AI and fine-tuned LLMs, we created a natural language interface to enterprise data that maintains high accuracy while being accessible to users without SQL knowledge.

Technical architecture: • Domain-specific fine-tuning on complex database schemas • Advanced prompt engineering and context management • Query validation and optimization pipelines • Integration with existing BI tools and dashboards • Real-time query performance monitoring • Automated learning from user feedback and corrections

The system successfully converted complex analytical queries that previously required data engineering support into self-service operations.

Key Challenges

  • Handling complex join operations and nested queries
  • Maintaining query accuracy with domain-specific terminology
  • Ensuring data security and access control
  • Managing computational costs of LLM inference

Outcomes & Impact

  • 30-40% improvement in query accuracy
  • 80% reduction in data engineering support requests
  • Faster insights delivery to business stakeholders
  • Improved data literacy across the organization

Technologies Used

GPT-4LangChainFine-tuningRAGSQLPython

Company

Bain & Company

2024