The original title is "Building a RAG System from Scratch — Design Decisions Explained"

Short summary

Explains key RAG architecture decisions: pgvector + PostgreSQL is cost-effective for most use cases, 768-dimensional embeddings balance quality and cost, asymmetric task types improve retrieval accuracy, and HNSW indexing handles production scale. Includes four-phase scaling strategy and guidance on when to upgrade embedding vs. generation models.

• •pgvector integrates with PostgreSQL, scales to millions of documents, cheaper than purpose-built vector DBs for most teams
• •768-dimension embeddings are the production sweet spot—full quality with faster index builds and lower storage costs
• •Use different task types for ingestion and querying to leverage asymmetric model training and improve accuracy

Generated with AI, which can make mistakes.