Comparison — RAG explained: a chatbot that uses your documents
Side-by-side analysis to help you choose
Connect a language model to your knowledge base with retrieval, citations and access controls.
Approaches compared
Search before generating
RAG retrieves relevant passages before asking a model to answer. This adds document context without retraining the model for every update. Search can combine keywords and semantic similarity to match the meaning of a question.
Example: an internal procedure
An employee asks how to request equipment. The assistant finds the approved procedure, explains the steps and links the source. If nothing relevant is found, it should say so. Citations help verification but do not guarantee correctness.
Respect document permissions
Filter documents by user permissions before retrieval reaches the model. Remove obsolete versions and test questions with known answers. Treat instructions inside retrieved files as untrusted data, not permission to perform actions.
Our recommendation : Retrieval quality and answer evaluation matter as much as the model.
Why Tuplify Studio?
We build document-search assistants with citations and permission-aware retrieval connected to your knowledge base.
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Tuplify Studio turns these concepts into working software.
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