Originally published on AI Tech Connect.
Why your vector DB choice matters more in 2026 For the first eighteen months of the retrieval-augmented generation era, the quality bottleneck sat firmly inside the language model. If your answers were wrong, the cure was a better model, a longer context window or a smarter prompt. That diagnosis no longer holds. By Q1 2026, with Claude 4.6, GPT-5.2 and Gemini 2.5 all clearing the same evaluation thresholds within a few percentage points of each other, the bottleneck has moved decisively upstream. Retrieval quality — what your vector database actually returns when a user asks a question — is now what separates a useful RAG application from an embarrassing one. That shift changes the way you should choose a vector database. The decision used to be a checklist exercise: pick the engine with…
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