RAG
Retrieval-Augmented Generation
AI searches your enterprise knowledge in real time
and generates accurate, context-aware answers
What is RAG?
RAG (Retrieval-Augmented Generation) is a technology that searches for relevant information before AI generates an answer, creating more accurate and reliable responses.
Retrieval
Finds relevant documents and information using hybrid search (Vector + Keyword)
Augmented
Adds retrieved information to the AI prompt
Generation
Generates accurate answers based on retrieved information
Relies only on training data → Lacks latest information, hallucination occurs
Real-time knowledge retrieval → Reflects latest information, source-based accurate answers
RAG Applications
RAG technology is applied across various QuantHow services
AI Development Workflow
Automatically searches past sessions, code patterns, and documentation in the Claude Code integrated AI collaboration system to provide development context.
- Auto Session Context Search
- Code Pattern Recommendations
- Documentation-based Answers
- Feedback Loop Learning
Web AI Agent
Generates accurate answers by real-time searching FAQs, manuals, and knowledge bases in enterprise-customized AI chatbots.
- Auto FAQ Response
- Document-based Answers
- Multilingual Support
- Domain-specific Learning
Quant Trading
Automatically provides relevant information for investment decisions by vector-searching historical market data, news, and reports.
- Market Data Analysis
- News Summary Search
- Report-based Insights
- Real-time Alerts
Domain-specific RAG Expansion
We are researching RAG systems specialized for professional domains
Patents
Patent law, examination guidelines, case law, prior art
Legal
Legislation, case law, contracts, legal advisory
Medical
Research papers, clinical trials, guidelines
Finance
Financial regulations, supervisory rules, interpretations
Tech Stack
Building reliable RAG systems with proven technology
5-Layer Storage
File, RDB, Vector, Search, Memory
Qdrant
High-performance Vector Database
Embedding
Multilingual Embedding Models
Hybrid Search
Vector + Keyword + Meta Filter
Benefits of RAG
Why should you adopt RAG?
High Accuracy
Hybrid search combining Vector (semantic) and Keyword (precision)
Knowledge Utilization
AI references internal documents, manuals, and history in real time
Data Security
Self-hosted servers prevent sensitive data leakage
Customization
Domain-specific embedding models and search strategy optimization
Implementation Process
We build RAG systems through a systematic process
Data Collection
Collect knowledge sources: documents, code, conversation logs
Embedding Generation
Convert text to vectors for semantic search
Indexing
Store and optimize in Qdrant vector DB
Search Integration
Connect RAG pipeline to AI system
Quality Improvement
Continuously improve search accuracy based on feedback
Want to implement an AI knowledge search system?
We'll propose a RAG solution tailored to your organization through a free consultation