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2026年6月29日 June 29, 2026

Google 把 25 年搜尋技術變成 RAG 服務 Google Brings 25 Years of Search Technology to RAG

#AI#RAG#Google Cloud#LLM#搜尋技術

大家都知道現在 AI（LLM）很厲害，但它有時候也會犯「一本正經胡說八道」的毛病。要解決這個問題，現在最紅的技術架構叫做 RAG（檢索增強生成）── 也就是給 AI 一個外部資料庫，讓它先查資料再回答。

那問題來了：誰是世界上的「查資料王者」？當然是 Google 🔍！

我看了一篇 Google 官方文章，深入淺出地介紹了他們怎麼把搜尋引擎的頂尖技術，拿來強化 RAG。

什麼是 RAG？

檢索增強生成（Retrieval-Augmented Generation，簡稱 RAG）是一種將大型語言模型（LLM）與外部知識來源結合的 AI 架構。它能讓模型在回答問題時，先從專屬或即時的資料庫中搜尋相關資訊，再依據這些檢索結果來生成答案，大幅提升內容的準確性並減少胡言亂語／幻覺（Hallucination）。

Google RAG 厲害在哪裡？

同時結合關鍵字搜尋與語意搜尋，再也不怕 AI 誤解你的問題。

文章舉了一個很直覺的例子——在商店輸入查詢詞 “dinosaur”（恐龍）和 “keyholder”（鑰匙圈）時：

  • 關鍵字搜尋會自動將查詢詞擴展至同義詞，例如 “dino” 與 “keychain”
  • 語意搜尋則會把 “keyholder” 視為意圖導向查詢（你其實在找恐龍主題配件），搜尋結果因此額外出現「恐龍掛繩」和「恐龍徽章」

兩者搭配，就算你問得不夠精確，也能撈出你真正要的資料。

2. 速度超快：ScaNN

Google 採用自家研發的 ScaNN（Scalable Nearest Neighbors） 語意搜索技術，能夠在海量資料中快速找到高度相關的文件，搜尋只需幾秒內完成。

3. 聯想能力強：Neural Matching（神經匹配）

就算你問得不精準，Neural Matching 也能自動聯想到正確的知識點。

例如你輸入「冬季保暖服裝」，系統可以自動比對出羽絨外套、刷毛外套等等結果，而不是只認識一模一樣的關鍵字。

結語

Google 正在把超過 25 年的搜尋技術積累，轉化為自家的 RAG 付費服務。企業和開發者可以透過 Google Cloud 購買這套服務，讓 AI 的搜尋與回答品質達到更傑出的水準。

延伸閱讀

Everyone knows AI (LLMs) are impressive — but they have a tendency to confidently make things up. The most popular solution to this problem is a technique called RAG (Retrieval-Augmented Generation): give the AI an external knowledge base so it can look things up before answering.

Which raises the question: who is the world’s undisputed champion of looking things up? Google, of course 🔍

I read a Google Cloud blog post that breaks down how they’re applying their world-class search technology to supercharge RAG.

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI architecture that combines large language models (LLMs) with external knowledge sources. Instead of relying solely on its training data, the model first searches a dedicated or real-time database for relevant information, then uses those results to generate its answer — dramatically improving accuracy and reducing hallucinations.

What Makes Google’s RAG Stand Out?

By combining keyword search and semantic search simultaneously, Google’s RAG handles both precise queries and intent-based ones.

The article gives a concrete example: searching a store for “dinosaur” and “keyholder”:

  • Keyword search automatically expands to synonyms like “dino” and “keychain”
  • Semantic search interprets “keyholder” as an intent-driven query (you’re looking for dinosaur-themed accessories), surfacing results like “dinosaur lanyards” and “dinosaur badges”

Together, even imprecise queries return what you actually wanted.

2. Blazing Fast: ScaNN

Google uses its own ScaNN (Scalable Nearest Neighbors) technology to perform semantic search at massive scale — finding highly relevant documents across enormous datasets in seconds.

3. Smart Association: Neural Matching

Even when your query is vague or off-target, Neural Matching bridges the gap to the right knowledge.

Type “warm winter clothing” and the system automatically surfaces results like down jackets and fleece coats — without needing an exact keyword match.

Conclusion

Google is packaging over 25 years of search expertise into a paid RAG service on Google Cloud. Businesses and developers can leverage this to dramatically raise the quality of AI-powered search and question-answering in their applications.

Further Reading