Retrieval Augmented Generation
Enhance AI responses with knowledge from your documents and data
How Retrieval Augmented Generation Works
Document Upload
Users upload documents containing knowledge
Document Processing
Documents are chunked and indexed for retrieval
Query Processing
User queries are analyzed to find relevant document chunks
Knowledge Integration
Retrieved information is integrated with the AI's knowledge
Response Generation
AI generates accurate responses grounded in document content
Retrieval Augmented Generation (RAG) enhances AI responses by grounding them in your specific documents and data. This approach combines the power of large language models with the accuracy and relevance of your proprietary information.
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How It Works
Your documents are processed, chunked, and indexed to create a searchable knowledge base that AI can reference.
When you ask a question, our system finds the most relevant information from your documents using advanced semantic search.
The AI generates responses grounded in your documents, providing accurate, contextual answers with proper citations.