RAG Question & Answer

What it gives you

• Retrieval-augmented generation (RAG) over indexed notes and whiteboards
• Hybrid lexical + semantic retrieval before generation
• Bounded context planning instead of sending the whole library to the model
• Local LLM answers grounded in retrieved evidence
• Source citations that link back to supporting notes

RAG instead of asking the model to guess

Before answering, Zettory retrieves the most relevant indexed note fragments and plans a bounded context for the local model. The answer is generated from that retrieved material rather than relying only on model memory.

Keep the source visible

Answers include citations to the underlying notes so you can inspect the evidence, open the source, and decide whether the response matches what you actually wrote.

Ask the library or focus on one note

Use the whole indexed library when you want synthesis across your knowledge, or narrow the question to a note when you want the answer grounded in a specific piece of context.