---
format: "aidr-story-markdown/v1"
id: "b21fa54b837dba6b3505f76961e5e0c8433748c069a6448982cb59402022e336"
canonical_url: "https://aidr.today/b21fa54b?lang=en"
title: "Faster Retrieval for Better, More Efficient RAG"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
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published_at: "2026-11-05T00:00:00.000Z"
category: "Data"
topics: ["llm","data-engineering"]
source_urls: ["https://machinelearning.apple.com/research/faster-retrieval"]
summary: "Maximum inner product search sits at the core of recommendation systems, search, and retrieval-augmented generation RAG. The computational cost of running an exhaustive comparison of the query to all keys grows dramatically as the number of keys grows to millions and vector dimensions reach thousands."
---

# Faster Retrieval for Better, More Efficient RAG

> [Open the canonical story](<https://aidr.today/b21fa54b?lang=en>)

**Published:** 2026-11-05T00:00:00.000Z
**Category:** Data
**Topics:** llm, data\-engineering

## Summary

Maximum inner product search sits at the core of recommendation systems, search, and retrieval\-augmented generation RAG\. The computational cost of running an exhaustive comparison of the query to all keys grows dramatically as the number of keys grows to millions and vector dimensions reach thousands\.

## Sources

- [Story source](<https://machinelearning.apple.com/research/faster-retrieval>)