What threshold works best for feedback clustering?

0.85 cosine similarity catches semantic matches like 'dark mode' pleas without over-grouping.

How do embeddings handle negation in user feedback?

They don't great — 'love dark mode' and 'hate it' vector close. Sentiment scores help filter.

Can I add pgvector to existing Supabase Postgres?

Yes, enable extension, use Unsupported in Prisma, raw SQL for ops.

4,950 API Calls to Zero: Embeddings Rescue Feedback Overload

100 feedback items? That's 4,950 pairwise comparisons if you brute-force it with Claude. One dev built smarter: embeddings turn text into vectors, letting Postgres handle the math.

theAIcatchup Apr 11, 2026 4 min read

Code snippet showing vector similarity query in Postgres for feedback grouping

⚡ Key Takeaways

Embeddings crush pairwise AI comparisons: 100 items = 4,950 calls vs. one-time vectors + fast DB math. 𝕏
pgvector on Postgres adds semantic search without new DBs — hybrid Prisma/raw SQL gets it done. 𝕏
Threshold at 0.85 + self-exclude fixes clustering; covers 80% of feedback cases out the gate. 𝕏

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theAIcatchup

Community-driven. Code-first.

#AI embeddings #pgvector #user feedback clustering #voyage ai

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