The story of how internal document search was achieved through Gemini's Google Workspace integration without building a RAG system

Introduction

When you want to search and leverage internal documents with AI, the first thing that comes to mind is probably building a RAG (Retrieval-Augmented Generation) system. Setting up a vector DB, splitting documents into chunks, generating embeddings… it involves considerable development and operational costs.

However, if your internal documents are consolidated on Google Drive, you might be able to achieve an equivalent experience without building RAG, simply by using Gemini's Google Workspace integration.

At Classmethod, we manage internal documents on Google Drive. When I tried out Gemini's Workspace integration feature, the setup was virtually zero effort, and the search accuracy and response quality were sufficiently practical, so I'll introduce the setup method and how it feels to use.

Prerequisites

• Your organization uses Google Workspace
• You have a plan that supports Gemini (Gemini Business / Enterprise, or Google One AI Premium, etc.)
• Your internal documents are stored on Google Drive

Setup Steps

The configuration to enable searching Google Workspace information with Gemini is very simple.

1. Open Gemini Settings

Go to Gemini and open the settings screen.

2. Enable App Integration

In the settings screen, click on "App integration".

3. Enable Google Workspace

Turn on the "Google Workspace" toggle.

That's all the configuration you need.

How to Use

Once setup is complete, simply use @workspace in the Gemini chat screen to ask questions.

Example: Checking Internal Procedures

For example, if you want to know "how to apply for the Claude Max plan," enter the following:

@workspace Please tell me how to apply for the Claude Max plan

Gemini searches for relevant documents within Google Drive and summarizes the content to provide an answer.

Since the response also includes links to source documents, you can quickly access the original document when you want to check the details.

Impressions After Using It

Here is a summary of the points I noticed after actually using it.

What Was Good

• Setup is virtually zero: You can start using it just by turning on the toggle. There's no need to prepare infrastructure or embedding generation pipelines like with RAG
• Response speed is fast: Searching and summarizing internal documents is smooth and stress-free
• Citing is solid: Links to the documents that serve as the basis for answers are clearly shown, making it easy to verify the reliability of information
• No operational costs: Just keeping documents on Google Drive is sufficient, with no need for additional sync or index update mechanisms

Things to Note

• Since this requires documents to be on Google Drive, coverage may be incomplete if documents are scattered across other storage solutions
• Searches are based on access permissions, so documents you cannot access will not be included in search results
• Search accuracy is also affected by how well documents are organized (file names, folder structure, and text quality within documents)

Things to Consider Before Building RAG

RAG is a versatile and powerful approach, but it's not necessary in every case. I think it's worth trying Gemini's Workspace integration first, especially when the following conditions are met:

• Internal documents are consolidated on Google Drive
• Search targets are primarily text-based documents (Google Docs, Spreadsheets, PDFs, etc.)
• The main goal is "easily searching internal knowledge with AI"

Building RAG involves considerable effort, including selecting and operating a vector DB, considering chunking strategies, choosing an embedding model, and building a data synchronization pipeline. When existing tools are sufficient, it's more rational to leverage them first.

Summary

With Gemini's Google Workspace integration, you can easily search and utilize internal documents without building RAG. The ease of use — configuration is just turning on a toggle, and usage is just asking questions with @workspace — combined with practical response quality and citation accuracy makes it very accessible.

Before thinking "let's build RAG for now," I recommend first trying to see how far you can get with the tools already at hand.