How semantic search works when your notes never leave the device
This future article should demystify semantic search without overselling AI or implying any cloud dependency. The draft brief keeps the eventual copy aligned with Cabinet's actual local-first position.
What the finished article should do
Teach the difference between keyword search and semantic search in plain language, explain why local indexing matters, and make it clear that Cabinet keeps AI optional and off by default.
Suggested outline
- Explain the limits of exact-match keyword search.
- Introduce semantic search without machine-learning jargon overload.
- Show why privacy-sensitive buyers care where indexing happens.
- Clarify the role of optional local AI and when a user might leave it off.
- Close with a grounded explanation of what Cabinet does and does not do.
Prompt for another LLM
Write a 1300-1600 word educational article for Cabinet titled "How semantic search works when your notes never leave the device". Explain keyword search versus semantic search, embeddings at a high level, and why local indexing matters for privacy. Describe Cabinet as AI-optional and disabled by default, with semantic search and Ask mode only when enabled. Keep the explanation accessible to non-ML readers and avoid implementation claims not visible on the site.
On-page SEO notes
- Use the term "semantic search" naturally in the title, lead, one H2, and conclusion.
- Link back to Optional AI and Local-first trust.
- Avoid implying passive desktop capture, cloud upload, or automatic AI enablement.