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Introduction

The most critical factor in building enterprise RAG (Retrieval-Augmented Generation) systems is high-quality domain-specific data. This guide presents verified public datasets and practical implementation methods available across 7 major industry sectors.

Guide Features

  • Free Access: All datasets are freely available
  • Structured Schema: Clear structures facilitating RAG system design
  • PoC Ready: Immediately applicable for enterprise customer demonstrations
  • Practical Code: Implementation examples provided for each dataset
  • Production Verified: Only datasets validated in actual operational environments

Industry Dataset Summary

Sector Recommended Dataset Data Scale Primary Use Cases
Banking FDIC Call Report 5,000+ institutions, quarterly Financial statement Q&A, risk analysis
Insurance NAIC InsData 3,000+ insurers Claims analysis, complaint handling
Accounting SEC XBRL 8,000+ public companies Financial item search, disclosure analysis
Legal CourtListener 4M+ cases Case law search, legal consultation
Healthcare MIMIC-IV 300K+ patients Clinical Q&A, diagnostic support
Automotive NHTSA API Hundreds of thousands of recalls Safety inquiries, recall notifications
Securities NASDAQ Data 15+ years of stock prices Investment analysis, trend prediction

1. Banking and Credit Analysis

1-1. FDIC Call Report

Overview: Quarterly financial statement data submitted by all FDIC-supervised banks

Data Structure: The dataset includes comprehensive banking information such as bank names, certification IDs, quarterly periods, total assets, tier 1 capital ratios, net income, and loan loss provisions.

RAG Implementation: The system can be implemented using LangChain document loaders for CSV data, recursive character text splitters optimized for CSV data with appropriate chunk sizes and separators, and vector stores using OpenAI embeddings. Bank profile chunks can be generated containing essential financial metrics and quarterly information.

Usage Scenarios:

  • 📊 Risk Analysis: Identifying common characteristics among banks with capital ratios below 10%
  • 📈 Performance Comparison: Determining bank positioning relative to similar-sized institutions
  • 🔍 Regulatory Compliance: Identifying items not meeting Basel III standards

Data Access: FDIC Call Reports

1-2. FRED API (Federal Reserve Economic Data)

Overview: Federal Reserve economic indicator database containing over 800,000 time series

Key Indicators: The database includes interest rates such as Federal Funds Rate and Treasury yields, money supply measures including M1, M2, and M3, employment indicators like unemployment rate and non-farm payrolls, and inflation metrics including CPI and PCE.

RAG Implementation: The system utilizes the FRED API to collect economic indicators, creates economic context with historical data analysis, and generates comprehensive reports including latest values, year-over-year comparisons, and historical extremes with corresponding dates.

Usage Scenarios:

  • 📊 Credit Policy: Determining loan portfolio adjustment directions in current interest rate environments
  • 📉 Economic Forecasting: Business cycle analysis based on unemployment rates and GDP growth
  • 💹 Asset Allocation: Optimal asset allocation strategies during inflationary periods

2. Insurance

2-1. NAIC InsData

Overview: Insurance company market data provided by the National Association of Insurance Commissioners (NAIC)

Data Components: The dataset includes market share and premium income, loss ratios and expense ratios, complaint counts and type-specific analysis, and financial soundness indicators.

RAG Implementation: The system categorizes complaint types including claim handling, policy service, premium billing, underwriting, and sales and marketing. Insurer profiles are generated containing company names, market shares, loss ratios, expense ratios, total complaints, primary complaint types, and financial ratings.

Practical Use Cases: The system enables insurer benchmarking by comparing competitors across multiple metrics including loss ratios, complaint rates, and market shares, generating comprehensive benchmark reports for strategic decision-making.

2-2. OpenFEMA NFIP Claims

Overview: Federal Emergency Management Agency (FEMA) flood insurance claims data

Data Characteristics: The dataset covers all flood insurance claims from 1978 to present, detailed classification by region and disaster type, and insurance payout amounts with damage assessment information.

Implementation: The system creates risk assessment contexts by postal code, analyzing historical claims data to generate risk profiles including past claim counts, average and maximum payout amounts, primary damage types, and calculated risk grades.

3. Accounting and Disclosure Materials

3-1. SEC XBRL Data

Overview: Securities and Exchange Commission (SEC) standardized financial statement data for public companies

XBRL Structure: The standardized format includes assets, liabilities, revenues, and other financial elements with contextual references and unit specifications for precise data interpretation.

RAG Implementation: The system parses XBRL files to extract key financial items including assets, liabilities, stockholders’ equity, revenues, net income, and earnings per share. Financial summaries are generated with company names, total assets, total liabilities, stockholders’ equity, revenues, net income, and earnings per share.

Advanced Query Processing: The system calculates financial ratios including current ratios from current assets and liabilities, debt ratios from liabilities and stockholders’ equity, and return on equity from net income and stockholders’ equity.

3-2. EDGAR 10-K Risk Factor Analysis

RAG Implementation: The system extracts risk factors from 10-K filings by locating Item 1A Risk Factors sections, extracting section content, and chunking risk factors into meaningful units. Risk factors are categorized into market, operational, financial, regulatory, and technology categories based on keyword analysis.

4-1. CourtListener

Overview: Legal database containing over 4 million US court cases

API Implementation: The system searches cases using query parameters, court specifications, and date ranges. Case summaries are generated including case names, courts, filing dates, panels, key issues, dispositions, and citation counts.

Legal Q&A System: The system identifies legal areas from questions, searches relevant cases based on jurisdiction context, and composes legal answers with relevant case law, providing disclaimers about consulting attorneys for specific legal advice.

4-2. Caselaw Access Project

Harvard Law School’s 360-Year Case Law Data: The system analyzes legal evolution by tracking legal concepts across time periods, identifying landmark cases and legal trends, and creating evolution narratives showing legal development processes with case counts, major decisions, and primary trends for each period.

5. Healthcare

5-1. MIMIC-IV v3.1

Overview: MIT’s ICU dataset containing over 300,000 patients with completed de-identification

Data Structure: The system processes multiple tables including admissions information, patient basic information, ICU stay information, chart records, lab results, prescription information, and clinical notes.

Medical Q&A System: The system classifies medical questions into diagnostic, treatment, prognosis, or general categories, handling each type with appropriate medical knowledge and similar case analysis.

Drug Interaction Analysis: The system analyzes prescription safety by checking drug interactions, assessing severity levels, and generating safety reports with appropriate warnings and recommendations.

5-2. PubMed Central OA

Medical Literature Evidence Search: The system searches evidence for medical questions by extracting medical terms, executing PMC searches, and extracting detailed paper information including titles, authors, abstracts, and URLs. Evidence summaries are generated with related research findings and appropriate medical disclaimers.

6. Automotive

6-1. NHTSA API

Overview: National Highway Traffic Safety Administration automotive safety data

API Implementation: The system retrieves vehicle information using VIN-based queries and recall information by manufacturer, model, and year. Safety profiles are created including manufacturer details, model information, model years, total recall counts, and major recall items with affected units and remedies.

Accident Data Analysis (FARS): The system analyzes accident patterns for specific vehicle makes and models, identifying common factors, seasonal trends, and generating safety insights with basic statistics, major risk factors, and seasonal dynamics.

7. Securities

7-1. NASDAQ Stock Data

Comprehensive Stock Data RAG System: The system retrieves stock data with technical indicator calculations including simple moving averages, RSI, and MACD. Stock analysis contexts are created with current prices, daily changes, 52-week highs and lows, technical indicators, and company information including market capitalization, P/E ratios, and dividend yields.

Portfolio Analysis System: The system analyzes portfolios by calculating portfolio returns, risk indicators including annual returns, volatility, Sharpe ratios, maximum drawdowns, Value at Risk, and correlation matrices. Comprehensive portfolio reports are generated showing composition, performance metrics, risk indicators, and correlation analysis.

RAG vs Fine-tuning Strategy Guide

Industry-Specific Optimal Approaches

The system provides optimal configurations for different industries. Banking applications use 512 chunk sizes with 50 overlap, text-embedding-ada-002 embeddings, Pinecone vector stores, and cross-encoder reranking models. Legal applications require larger 1024 chunk sizes with 100 overlap for longer context needs, sentence-transformers embeddings, Weaviate vector stores, and enhanced reranking models. Medical applications use smaller 256 chunk sizes with 25 overlap for accuracy, all-mpnet-base-v2 embeddings, Qdrant vector stores, and specialized reranking models.

Hybrid Approach Implementation

The hybrid system implements three-stage development: Stage 1 involves RAG system deployment with vector store construction for each industry. Stage 2 collects user Q&A logs during monitoring periods, evaluating RAG response quality and identifying improvement candidates with quality scores below 0.7. Stage 3 performs fine-tuning on selected data, preparing training data and creating fine-tuning jobs with improved answers combined with RAG-retrieved contexts.

Security and Compliance Considerations

Data Security Framework

The secure RAG framework implements row-level security with role-based access rules for different user types including banking analysts, legal counsel, and medical researchers. Each role has specific allowed tables, restricted fields, and data masking requirements.

Data Governance Implementation: The system classifies data sensitivity, determines retention periods, enables access logging, and implements encryption at rest and in transit.

GDPR and Privacy Compliance

The privacy-compliant RAG system ensures GDPR compliance through PII detection, automatic anonymization, and implementation of right to erasure by finding and marking user data for deletion while updating vector embeddings.

Performance Optimization and Monitoring

RAG Performance Metrics

The performance monitoring system measures retrieval quality through precision and recall, context relevance, answer relevance, faithfulness, and completeness. Performance indicators include response times, token usage, and cost per query.

Automatic Performance Tuning

The auto RAG optimizer optimizes chunk sizes by testing various sizes with performance evaluation, and optimizes retrieval parameters through grid search across top-k values, similarity thresholds, and rerank parameters.

Practical Implementation Example

Multi-Industry RAG Platform

The comprehensive platform includes industry processors for banking, insurance, legal, medical, automotive, and securities sectors. The system initializes industry-specific RAG systems through data preprocessing, vector store construction, and RAG pipeline setup.

Multi-Industry Querying: The system handles queries across multiple industries based on user permissions, integrates results from different sectors, and ranks sources by confidence scores to provide comprehensive integrated answers.

Conclusion

The 7 industry sectors’ verified public datasets presented in this guide can serve as starting points for building enterprise RAG systems.

🎯 Key Success Factors

  1. Gradual Construction: Progressive development from RAG to monitoring to fine-tuning
  2. Industry Optimization: Chunking and embedding strategies tailored to each domain’s characteristics
  3. Security First: Consideration of data governance and access control from the beginning
  4. Continuous Improvement: Establishment of performance monitoring and automatic optimization systems

🚀 Next Steps

  1. PoC Phase: Start with 1-2 industries to validate core functionality
  2. Expansion Phase: Add other industry areas based on success cases
  3. Advanced Phase: Support multimodal data and real-time updates

Please feel free to ask for additional implementation guides for specific industries or detailed dataset utilization methods! 🤝