How To Use RemNote: Practical Note-taking Advice & Flashcard Types

RemNote is built for turning nested notes into a connected knowledge base, then converting selected pieces of that structure into flashcards with built-in spaced repetition. The core workflow is an “accordion” hierarchy—broad topics down to increasingly specific concepts—paired with backlinking and tagging that link related ideas across a workspace, so studying becomes more than collecting facts; it becomes navigating a web of meaning.

Notes start with a sidebar organized into sections like pinned, draft, and finished. From there, users create hubs (folders or documents) that act like dashboards: everything inside a hub is tied to the hub’s title. The hierarchy uses heading levels as the backbone—H1 for the broadest topic, H2 for main subtopics, and H3 for section breakers. Each heading automatically creates a “power up” tag-like element, letting users jump across all headings of the same level throughout the workspace. Portals extend this idea: they can display and even edit content from other places, enabling quick cross-navigation (for example, pulling in decks from a French hub into a different hub).

Flashcards are created directly from the note structure using punctuation-based controls. A multi-line flashcard is made by using double colons “::” next to a label; the card can prompt recall either forward (show the question first) or backward (show the answer first), depending on the practice direction. Plain text lines remain non-flashcard content, while “::” turns them into prompts. The most important design choice is how much context appears on the back of a card: RemNote’s hierarchy means flashcards can automatically include parent headings and related descriptors, which is useful for context but can become noisy if questions are nested too deeply. When that happens, restructuring—such as tab-indenting questions under the specific event list—keeps the back of the card focused on only what should be referenced.

RemNote also supports different flashcard types for different learning patterns. A “set” uses a dash-style list so the card prompts items one at a time, which works well for sequences like cause → event → effect. Multi-line cards can ask for multiple descriptors at once, while sets help learners build stepwise recall. Another feature, “close,” works like Anki-style cloze deletions: users can create cards that hide specific terms within a sentence, then merge multiple closes into a single prompt when both terms should be recalled together.

Once notes are structured, flashcards are accessed through a dedicated flashcard view (the “q” button), where practice can run with spaced repetition or without it. Learners can type answers or recall them, then mark results using quick keys (e.g., wrong/right/known-too-early). Flashcards can also be edited later, with signals returning the learner to the exact concept in the notes.

Finally, the system’s tagging and linking features turn the hierarchy into a navigable graph. Users can link summary descriptors to other notes, use hashtag-based related content, and even filter by color via highlighting power-ups. Practical extras include dark mode and mention of newer features like a Pomodoro timer for flashcards. The takeaway is a single integrated loop: nest ideas, link them, convert the right parts into flashcards, and study in a way that reinforces the structure of understanding—not just isolated memory.