Unlock the full potential of DNote AI by asking natural language questions that draw from both your private data and the global web.
Contextual RAG (@mydata)
Retrieval-Augmented Generation (RAG) is what makes DNote AI truly unique to your workflow.
- Data Anchoring: Use the `@mydata` tag to force the AI to search your private workspace before answering.
- Scoped Queries: Ask questions like 'What was discussed in my meeting yesterday?' or 'Find my project milestones' to get context-aware answers.
Automated Intent Detection
Our AI engine automatically detects the best tools to use based on your question.
- Smart Tool Selection: If you ask for a summary, Arka chooses the Summarizer. If you ask about a project, it triggers a workspace search.
- Manual Command Overrides: Use commands like `@research` or `@qna` to explicitly tell the AI which mode to prioritize.
AI Ranking & Relevance
Arka uses semantic vector embeddings to find the most relevant information.
- Conceptual Matching: Results are ranked by meaning, not just exact keywords.
- Multi-Source Synthesis: The AI can pull snippets from multiple notes and files to build a comprehensive answer.
Step-by-Step: Asking Questions
Interact with your collective knowledge using natural language:
- Type your question into the AI Toolkit glass bar.
- Tag Data: Add `@mydata` to focus the AI on your private workspace docs.
- Intent Analysis: Wait 1-2 seconds as Arka analyzes your intent and searches for relevant context.
- Verify Sources: Hover over the clickable citations in the AI's response to see which note or file the information was sourced from.