Simplify and Organize Your Tags in Mem.ai:

Tagging in Mem becomes useful only when it’s organized around a small set of repeatable “contexts,” not an ever-growing list of topics. The core finding is a “two tag rule”: apply one overarching context tag (what kind of thing it is) plus one additional status or grouping tag, and keep the total number of tags per note low. When tags multiply by topic, discovery turns into manual sifting—hundreds of notes share the same label, and the user has to hunt for what’s actually relevant.

The transcript contrasts topic-based tagging with contextual tagging. Topic tags feel intuitive—tag a note “neuroscience,” for example—but topics are effectively infinite and quickly produce clutter. Instead, the approach prioritizes actionability and retrieval: tags should help a person find the right note quickly, without needing to remember where it lives or which topic bucket it belongs to.

A practical example comes from blog work. Notes are tagged with a prefix-based structure such as “uc blog,” where the prefix groups everything under a single context. Clicking that tag surfaces every related blog note, while a second tag captures the stage of work—such as “works in progress” versus “ready” or “published.” This keeps the context stable and the workflow status explicit, so the user can scan only the subset they need.

The same pattern applies to podcast transcripts. Instead of tagging each transcript by topic, the transcripts are grouped under a single context tag like “uc transcript.” Because each transcript is named by the guest, the user can locate a specific interview by searching for the person’s name, while the tag ensures all transcripts remain easy to retrieve.

A project example shows how contextual tagging scales. For a “jobs to be done” project tied to the “demand site sales” framework, the notes are organized by resource type and project context. “Customer interview” becomes the key context tag for interview material, while other deliverables—like landing page copy and email sequences—are retrieved by their resource-type tags such as “landing page copy,” “launch sequences,” and “campaign email.” The result is that searching by tag instantly pulls together all versions across multiple products, eliminating the need to remember which project folder or topic label contains the item.

The transcript’s takeaway is straightforward: limit tags per note, tag by context more often than topic, and treat projects as a partial exception because they naturally require more metadata. If someone doesn’t have a company prefix to use, initials can replicate the same structure. The overall goal is a tagging system that stays small, consistent, and fast for retrieval as the knowledge base grows.