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Zettory Guide

How RAG Question Answering Works Over Personal Notes

RAG retrieves relevant note context before generation so answers can be grounded in your own knowledge and linked back to sources.

Retrieval comes before generation

A RAG workflow first searches the indexed knowledge base for relevant passages. Those passages become context for the language model.

Hybrid retrieval covers different query styles

Exact words are useful for names and specific phrases, while semantic embeddings can surface related concepts when the wording differs.

Context should be bounded

A retrieval layer selects the strongest evidence instead of feeding the entire library into every question.

Citations keep the answer inspectable

The model response is more useful when you can open the underlying note and verify what was actually written.

Zettory keeps retrieval and generation local

Knowledge Base combines lexical retrieval with local semantic embeddings, selects a bounded set of evidence, and passes that context to the local language model. Answers return citations to the supporting notes.

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