EPISODE · May 27, 2026 · 18 MIN
Production RAG That Actually Works: Supabase pgvector with Bounded Corpus and Auto-Refresh
from Headcount Zero · host Jordan
Most private RAG deployments fail not because of the model, but because of three fixable infrastructure problems: stale embeddings from unchanged source documents, unbounded corpora that contaminate results with irrelevant content, and no retrieval guardrails to catch bad matches before they reach the LLM. This episode walks through building a production Supabase pgvector RAG system that solves all three: automatic re-embedding when source documents change, bounded corpus design, and SQL-level retrieval gates that block the LLM call when similarity scores are too low or keyword overlap is insufficient. You'll get the complete schema, indexing strategy, refresh pipeline using Make.com or n8n, and the exact cost breakdown for running this on Supabase Pro.
What this episode covers
Most private RAG deployments fail not because of the model, but because of three fixable infrastructure problems: stale embeddings from unchanged source documents, unbounded corpora that contaminate results with irrelevant content, and no retrieval guardrails to catch bad matches before they reach the LLM. This episode walks through building a production Supabase pgvector RAG system that solves all three: automatic re-embedding when source documents change, bounded corpus design, and SQL-level retrieval gates that block the LLM call when similarity scores are too low or keyword overlap is insufficient. You'll get the complete schema, indexing strategy, refresh pipeline using Make.com or n8n, and the exact cost breakdown for running this on Supabase Pro.
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Production RAG That Actually Works: Supabase pgvector with Bounded Corpus and Auto-Refresh
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