Using the AI prompt workspace
What the in-product prompt assistant can do, how sessions are stored, and why exports are part of the chat workflow.
Prompting is document-specific, not a generic chatbot
The default assistant message explicitly asks questions about business documents and gives export examples such as showing invoices and exporting to CSV.
This is a retrieval-and-action surface tied to document data, not a marketing assistant or general support bot.
Sessions and history matter
The UI can create new chats, switch active sessions, fetch prior messages, and reopen session history. The backend stores titles derived from the user prompt, which means recurring analytical conversations are expected.
The assistant can return files
The prompt page has dedicated logic for `downloadUrl`, `fileName`, and downloadable message variants. Knowledge content should teach users that some prompts return datasets, not only plain text responses.
Credit awareness is built into the experience
The UI logs token usage metadata in the browser console per action and model. Even if end users never inspect that, it is a sign that prompt usage has operational cost and should be documented with concrete examples.
Continue with adjacent workflows
Client onboarding and first login flow
How Datamonster decides whether a user lands on Get Started or Dashboard, and what the onboarding checklist actually tracks.
Document statuses, approvals, and archives
How list tabs and status mutations are wired, including processing, accurate, approval, and archive transitions.
Dashboard views by role
What each role sees on the main dashboard and why the dashboard is really a routing layer into operational queues.