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Why Production RAG Pipelines Need More Than a Vector Database on AWS

This story is from 2026-09-15. It is preserved in the archive; the latest stories are on the live feed.

My first RAG prototype did exactly what the tutorials suggested. The API received a document, split it into chunks, generated embeddings, and inserted them into a vector database. Then it queried the database, passed the context to an LLM, and returned the answer. It worked perfectly for a demo. It…

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  1. 2026-09-15 04:42 · DEV Community — AI
    Why Production RAG Pipelines Need More Than a Vector Database on AWS

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