Tagging Systems That Actually Work in Real-Life Setups

1. Why the same tag structure breaks across Notion and Obsidian

I’d set up a neat tag system in Obsidian—using # prefixed terms in Markdown—and I wanted to port the same structure into Notion for a shared team workspace. Notion doesn’t use inline tags the same way. Labels there live inside database properties, so anything imported from Obsidian just looked like plain text unless I did a manual migration.

This turns annoying quickly. You end up doing regex clean-ups just to convert #meeting-notes into a multi-select field. Worse, there’s no dynamic tag discovery in Notion. If your Notion DB has a tag property, you need to predefine all possible tags or wait for a human to click “add new.” It doesn’t auto-pull based on content, so the same tag can exist with three typos across multiple entries unless you’re cleaning it constantly.

I tried setting up a Make.com scenario to sync tags across Obsidian (via Git and a local folder integration) and Notion via its API. It works in theory, but Notion’s API throws a silent 400 if the multi-select property doesn’t include the tag you’re trying to assign—no error message in the UI, no webhook retry. The Zap just sits there like it worked.

Eventually landed on this compromise: tags are flat and predictable in Notion (predefined list only), and Obsidian stays wild-west with hashtags. Then I built a tag-mapping CSV and used a Python script with PyNotion to update allowed tags weekly. Feels ridiculous, but it works for now.

2. Using Airtable linked records as contextual tags not categories

People default to Airtable tags as single or multi-select dropdowns, but if you’re serious about navigating knowledge faster, that’s a bottleneck. Instead, I treat tags as linked records—tiny mini-tables of interrelated concepts—and the difference is night and day.

Example: I have a “Concepts” table with entries like “LTV”, “Churn”, “Account-Based Marketing”. Instead of tagging a doc with a label, I link it to one or more concepts. Now I can open a concept and see all docs, datasets, canvases, discussions linked to it. It’s not just metadata—it becomes navigation.

The key trick is turning the tag-hub into a first-class table where each tag has a description, timestamps, a source reference, maybe even a Notion link. You realize quickly that “tags” were just poor man’s relationships all along.

I once saw a lead-gen team spend four hours rebuilding a lookup table just because the tags stopped matching after a schema change. If they used linked records, the data would self-heal.

The gotcha: mass-tagging many rows via linked records is a pain in stock Airtable. You have to script it using the scripting block or external automation—Zapier can do some of it, but Airtable’s Zapier integration still chokes on multiple select-linked fields. I ended up syncing a Google Sheet and pushing updates through Make.com where I had more control over looping logic.

3. When tag filters in Coda silently ignore whitespace mismatches

Was building a personal CRM in Coda and had tags like “Freelance”, “Client”, “Newsletter-Lead”. Filtered a view by “Tag = Client” and… random people who weren’t clients showed up. Turned out one of the tags was “Client ” (with a trailing space). No visual indicator. No error. No match filtering either—you just get back wrong stuff.

Yes,
Freakishly enough, Coda treats whitespace inconsistently depending on context. A name with a trailing space can appear identical in UI, but fail equality checks in formula filters. No trim-on-ingest, no standardization. Bad when humans type fast and autosuggest doesn’t force matches.

Undocumented workaround that actually helps

I now pipe all tag inputs through a hidden formula field:

Lower(Trim(thisRow.Tags))

Then build all views atop that. It’s an extra step, but it prevents silent mismatches. Also helps whenever you export back to CSV to run audits—at that point, I can compare trimmed lowercase wordsets using Python’s difflib and catch drift.

A colleague once thought our recruiting pipeline broke—really, “Interviewed” vs “Interviewed ” were being treated as distinct values.

4. Extracting tags from GPT-4 outputs for research clippings

I collect a ton of research clippings from press releases. Initially, I was tagging snippets manually in Notion. Then tried auto-tagging with GPT-4—except the tags I got were way too abstract. Stuff like “industry trends”, “strategic moves”. Zero portability.

Eventually figured out you can prompt GPT-4 to act more like a StackOverflow taxonomy by grounding it in public tag sets. My prompt now includes:

“Only return up to five tags matching existing categories in the TechCrunch article database or standard Crunchbase fields.”

That changed everything. Suddenly “IPO,” “Biotech,” “Acquisition,” show up, and I can build useful filters. Sounds obvious, but it wasn’t until I saw GPT deliver “Disruption” five times in a row that I rewrote the prompt.

One edge case I hit

The GPT-4 API silently truncates tags if the string hits token limits. So if you’re returning full summaries AND asking for tags, keep them cleanly split with delimiters. I had one flat JSON output where the tags field just dropped off mid-word. Took me half an hour to realize it had nothing to do with the prompt quality and everything to do with token budget.

Also: long-form replies from the GPT-4 API sometimes include explanations even if your prompt says “return tags only.” Learned to lock it down with:

{"format": "Return only a valid JSON array of strings."}

Otherwise you’ll get: “Here are five tags: [list]” embedded inside the output, which breaks parsers expecting clean JSON. And yeah—Zapier’s Code by Zap step doesn’t catch those unless you manually error-handle raw string parsing.

5. Using tag synonyms in n8n to merge content idioms

Corporate users call it “Vendor Onboarding.” My notes say “Supplier Setup.” Legal calls it “Third-Party Activation.” Three sources. Same thing. So the smart move was building a tag synonym map using n8n.

I made a JSON node in n8n with something like this:

{
  "vendor onboarding": ["supplier setup", "third-party activation", "vendor registration"]
}

Then in my parsing workflow (pulling data from Notion pages + Airtable records), I ran each tag candidate through a custom Function node that looked up the standard tag based on synonyms. It effectively normalized everything upstream.

This allowed a search for “vendor onboarding” to catch notes using any of the above idioms, and for new entries to self-categorize via fuzzy match.

Hidden bug I ran into

If the Function node throws an error mid-run (e.g. if the synonym map is malformed), n8n retries the whole execution—but that means multiple records get updated redundantly unless you debounce or lock state. It cost me three backups before I realized I needed to stamp every run with a UID and skip if already applied.

Honestly, once this setup was in place, it surfaced a lot of scattered insights. We found seven duplicate vendor intake policies sitting in different folders under different names. Put differently: the synonym map didn’t just clean data—it also exposed hidden structure.

6. The hidden setting in Notion that corrupts tag relations

This one wasted days. In a relational Notion database, there’s a “show on both databases” toggle when you link two tables. I had a notes database linked to a “Topics” table (used like tags). I turned that toggle off on the Topics side because I didn’t want clutter.

Broke everything.

When “show on both” is disabled, Notion stops writing back the relation metadata to the linked item. So if you filter by reverse relation (e.g. show me all Topics with at least one linked Note), it silently returns an empty list—even though the Notes still link to Topics. Users see this as “Tags missing from Topics.” Worse, if you then duplicate the table, the relations vanish entirely.

I watched a teammate ask why new tags weren’t showing up. On her screen, the Topics list looked untouched. No errors. Because ALL the updates went one way only—into dead air on the other side of the table link.

There’s no warning. No tooltip. Just a silent state where relations become empty shadows.

To fix it, you have to re-enable the “show on both” switch, then re-save at least one record to trigger a re-sync. I ended up building Zapier websockets to detect the relation drift and flag it via Slack—not because I wanted to, but because I didn’t want to lose another week chasing phantom data holes.