What Is RAG (Retrieval-Augmented Generation)?
RAG (Retrieval-Augmented Generation) means an AI model first retrieves relevant passages from an organisation's own documents and grounds its answer in them. Answers can cite sources and the risk of hallucination is reduced, but not eliminated.
How does it work?
- Documents are parsed and split into chunks.
- The chunks are indexed for semantic search (embeddings).
- When a user asks a question, the relevant chunks are retrieved.
- The model takes these chunks as context, produces the answer and cites the source.
- Answer quality is measured regularly with an evaluation set.
Where is it used?
- Internal knowledge base and policy assistants
- Customer support assistants
- Regulation and document search
- Technical documentation question answering
Points to watch
- Answer quality depends heavily on retrieval quality.
- Access rights must be preserved; users should only get answers from documents they are allowed to see.
- The index must be refreshed as documents change.
- The risk of hallucination is reduced but not eliminated (Hallucination).
What does Huaris AI do about it?
Huaris AI designs RAG-based assistants under Custom AI Solutions and makes source citation and evaluation tests part of the system.
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