Retrieval-Augmented Generation (RAG)
RAG connects your AI agent to your documents - so answers come from your data, not guesswork.
A technique that enhances AI responses by first retrieving relevant documents from a knowledge base and then using those documents as context when generating an answer - combining the accuracy of search with the fluency of language models.
Document Retrieval
When a question is asked, the system searches your uploaded documents and finds the most relevant sections.
Contextual Generation
The AI uses retrieved documents as context to generate accurate, source-backed answers.
Source Attribution
Every answer can reference the specific document and section it drew from - enabling verification.
Living Knowledge Base
Update documents at any time. The retrieval system reflects changes without model retraining.
Hallucination Reduction
By grounding responses in real documents, RAG significantly reduces AI making up information.
Coverage Analytics
Track which documents are retrieved most and which questions have no matching documents - revealing knowledge gaps.
FAQ
How is RAG different from training the AI on my data?
Training changes the model itself (expensive, slow, risky). RAG keeps the model unchanged and feeds it your documents at query time (cheap, instant, safe).
What types of documents work with RAG?
PDFs, Word documents, web pages, spreadsheets, and text files. Structured and unstructured content both work.
Can RAG handle confidential documents?
Yes. Documents stay in your infrastructure. Access controls determine which users can query which document sets.
Ground your AI in real data
Learn how RAG powers accurate AI agents.