# SQL Generation and RAG for Financial Data Q&A Chatbot at Q4 Inc. ## Company and Use Case Overview Q4 Inc. is a capital markets access platform provider helping issuers, investors, and sellers connect effectively. They needed to build a chatbot for Investor Relations Officers (IROs) to analyze diverse financial datasets including CRM, ownership records, and stock market data. The system needed to: - Provide quick access to financial information through natural language queries - Integrate public and proprietary data while maintaining security - Deliver responses within seconds - Ensure high accuracy due to financial industry requirements - Maintain data privacy and security - Stay commercially feasible ## Technical Challenges and Solution Evolution ### Initial Approaches and Challenges The team explored several approaches before finding their solution: - Pre-training LLMs - Fine-tuning - Traditional RAG with semantic search ### Final Solution Architecture The team developed a novel approach combining SQL generation with RAG: - Step 1: SQL Generation - Step 2: Context Retrieval - Step 3: Response Generation ## Technical Implementation Details ### Key Components - Amazon Bedrock - LangChain - SQLDatabaseChain ### Performance Optimization - Model Selection Strategy - Prompt Engineering - Process Optimization ## Production Deployment - Microservices Architecture - Security and Compliance ## Results and Learnings ### Achievements - Successfully handled numerical and structured datasets - Achieved single-digit second response times - Maintained high accuracy in SQL generation - Cost-effective implementation - Scalable production deployment ### Key Learnings - SQL generation superior to traditional RAG for numerical data - Model selection crucial for cost/performance balance - Proper prompt engineering essential for accuracy - Framework usage (LangChain) reduced development effort - Multiple model approach allows optimization per task ## Best Practices Identified - LLM Selection - Prompt Engineering - Architecture - Development Process ## Future Considerations - Continuous monitoring of model performance - Regular evaluation of new model options - Performance optimization opportunities - Scaling strategy for increased usage - Security and compliance updates as needed