Choosing Embedding Dimensionality for Cost and Quality (ANSWERED)
Medium vector DB / embeddings trade-off question on dimensions vs cost/quality.
TL;DR — Quick Answer
Benchmark recall@k and downstream answer quality on your golden set across dimensions, then model storage and QPS cost. Often mid dimensions win on cost/quality; use Matryoshka or truncation only if the model supports it and evals confirm.
The Interview Question
Your embedding model offers 384, 768, and 1536 dimensions. How do you choose dimensionality for a 50M document RAG corpus?
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