Productive Life Wiki for Notion: Optimize with Global Tags (Free Template)
Based on Red Gregory's video on YouTube. If you like this content, support the original creators by watching, liking and subscribing to their content.
Create a single “Global tags” database with a “type” select property so each tag category can be separated via filtered views.
Briefing
A “global tags” database in Notion can act as a hub that organizes many other databases at once—by turning tag options into dedicated sub-views and then wiring those tags back to notes, tasks, projects, media, and even travel and contacts. Instead of relying on per-database select or multi-select fields, the system centralizes tags in one place and uses relations (plus linked database views) so metadata stays consistent as the workspace grows.
The build starts by creating a new database called “Global tags” as a full-page gallery. The tutorial then reshapes the default page into a tag card with a multi-select property (“tags”) that’s immediately converted into a single-select (“type”), renamed to “type.” Each tag option (like area, month, rating, glossary, and place) becomes its own filtered view. From there, the workflow duplicates views repeatedly—creating separate gallery/list sections for areas, months, ratings, glossary, and places—each with its own icon and filter (e.g., type = area, type = month, etc.). This turns one tag database into a structured taxonomy.
Next comes the relational wiring. A separate “Notebook” database connects to Global tags through a relation property (“area”), creating backlinks so each area tag automatically collects its related notes. The same pattern repeats for tasks: tasks relate back to Global tags via area, with a backlink called “tasks.” Contacts also connect to Global tags via area, and the tutorial emphasizes that these backlinks will accumulate over time.
To make the hub usable, the tutorial builds templates that populate each tag page with the right linked content. For example, an “area” template hides raw relation properties and embeds linked database views for tasks, notes, and contacts filtered to “area contains [current tag].” That template is then set as default for the Areas view, so creating a new area automatically generates the correct dashboard inside the tag page. The same template strategy is applied to months (linking blog calendar entries and projects) and ratings (linking media and published blog feedback).
Glossary tags take a different approach: instead of relations, the tutorial uses inline linking. A “new glossary item” template includes an AI-generated summary block (via a summary AI block) and is set as default only for the Glossary view. When a word like “Berlin Wall” is turned into a glossary term from within a note, a new tag page is created and backlinks appear in the note.
Finally, places function like an atlas. A “new place” template links to travel planner events (filtered to done), links to contacts (filtered by location), and adds AI content blocks to summarize the place and describe average weather. The template is set as default for the Places view, so creating a new city automatically generates a travel-journal-style page with the right linked data and generated narrative.
Overall, the system matters because it replaces scattered tag logic with one authoritative tag hub—then uses relations, backlinks, linked database views, and templates to keep every downstream database synchronized without manual re-tagging or duplicated configuration.
Cornell Notes
The tutorial builds a “Global tags” database in Notion that centralizes tags and turns each tag type (areas, months, ratings, glossary terms, places) into its own filtered view. Relations connect other databases—like Notebook, Tasks, Projects, Media, Blog Calendar, Travel Planner, and Contacts—back to Global tags, so each tag page automatically gathers related items via backlinks. Templates then embed linked database views filtered to the current tag, creating dashboards inside tag pages (e.g., an Area page shows tasks, notes, and contacts for that area). Glossary uses inline linking plus an AI summary block to generate a summary for each new term. Places use linked views and AI content blocks to summarize cities and show related travel events and contacts.
Why replace per-database select/multi-select tags with a Global tags hub?
How does the tutorial turn tag options into structured subsections inside Global tags?
What’s the mechanism that makes a tag page “collect” related content?
How do templates make tag pages useful without showing raw relation fields?
Why does Glossary use inline linking instead of relations?
What makes the Places setup feel like an “atlas” or travel journal?
Review Questions
- If Global tags stores all tag types, what specific property and view-filtering approach ensures each tag type (areas, months, ratings, glossary, places) stays separated?
- In the Area template, what linked database views are added, and how are they filtered so they only show items belonging to the current area tag?
- How does inline linking in the Glossary workflow create new pages and backlinks differently than relation-based linking?
Key Points
- 1
Create a single “Global tags” database with a “type” select property so each tag category can be separated via filtered views.
- 2
Duplicate and rename filtered views (Areas, Months, Ratings, Glossary, Places) so tag creation happens inside the right category.
- 3
Connect other databases to Global tags using relation properties (e.g., Notebook → area, Tasks → area, Contacts → area) to generate backlinks automatically.
- 4
Build templates for each tag category that hide raw relation fields and embed linked database views filtered to the current tag.
- 5
Set templates as default per view (e.g., default only on Areas view, Months view, Ratings view, Glossary view, Places view) so new tags instantly get the right dashboard.
- 6
Use inline linking for Glossary terms so selecting a term from text creates a new tag page and shows backlinks in the source note.
- 7
For Places, link Travel Planner (done events) and Contacts (location-based) and add AI content blocks to generate city summaries and weather descriptions.