"We tried asking ChatGPT about our internal processes, but it couldn't answer." — RAG (Retrieval-Augmented Generation) is the technology designed to solve exactly this problem. By combining a search of your company's documents, manuals, and FAQs with a generative AI, RAG lets the AI answer based on your own internal data. This article explains how RAG works in plain language, why it's effective for internal document search and FAQ automation, how it addresses hallucinations (AI errors), cost benchmarks, and how to get started.
- RAG (Retrieval-Augmented Generation) combines generative AI with a search of your internal documents, so the AI answers based on your own company data.
- It works in 4 steps: (1) ingest and index documents, (2) retrieve relevant documents, (3) pass the question plus documents to the AI, and (4) generate a grounded response.
- Because answers are grounded in real documents, RAG significantly reduces hallucinations — but cannot eliminate them entirely. High-stakes tasks should be designed with human review as a required step.
- Indicative cost: SaaS-type tools run from tens to hundreds of thousands of yen per month. MRI Inc. offers custom RAG from ¥0 initial cost and ¥98,000/month (excl. tax); costs vary by scope.
What is RAG? (Internal-Data AI)
RAG stands for Retrieval-Augmented Generation. In simple terms, it is a technique that combines generative AI with a search of your internal data, enabling the AI to answer questions based on your company's own information.
Generative AI tools like ChatGPT are trained on vast amounts of public internet content — giving them broad general knowledge. However, they have no knowledge of your company's internal information — your own regulations, manuals, or past inquiry records. As a result, asking them company-specific questions often produces off-target answers or convincing-sounding fabrications known as "hallucinations."
How RAG Works (4 Steps)
RAG's operation can be broken down into four main steps.
| STEP 1 Ingest & index internal documents |
Internal documents (manuals, regulations, FAQs, meeting minutes, etc.) are loaded into the system, converted into a searchable format (vector embeddings), and stored in a database. This preparation step is done once upfront. |
|---|---|
| STEP 2 Retrieve relevant documents (Retrieval) |
When a user enters a question, the system automatically searches the database and retrieves the internal documents that are semantically most relevant to the query. |
| STEP 3 Pass question + documents to the AI (Augment) |
The retrieved document content is passed to the generative AI as factual context, alongside the user's original question. |
| STEP 4 Generate a grounded response (Generation) |
The generative AI produces an answer based on the internal document content. It can also cite which documents it drew from. |
Use Cases for Document Search & FAQ Automation
Here are the tasks and scenarios where RAG is most effective.
| ① Internal helpdesk & automated inquiry responses | Automatically answers staff questions by searching company regulations, HR policies, IT manuals, and past Q&A cases. Handles repetitive inquiries like "How do I apply for leave?" or "When is the expense claim deadline?" without human intervention. |
|---|---|
| ② Customer-facing FAQ & support automation | Grounds AI responses in product manuals and past support cases. Delivers consistent service quality while reducing the man-hours spent on customer support. |
| ③ Sales & proposal knowledge search | Instantly surfaces relevant information from past proposals, case studies, and product databases. Cuts the time sales staff spend searching for the right information. |
| ④ Key-point extraction from large document archives | Enables instant search across accumulated meeting minutes, reports, and emails. Answer questions like "What decisions were made in the X project?" or "What issues came up last month?" |
For more on automating meeting minutes, see Automating Meeting Minutes & Transcription [2026]: AI Tool Selection, Accuracy & Adoption Tips.
Why RAG Works / Addressing Hallucinations
There are three main reasons RAG is highly regarded for internal business use.
① Grounded in facts — significantly reduces hallucinations — Because the AI answers based on actual internal documents, it dramatically cuts the fabricated responses that ungrounded AI can produce.
② Cites its sources — The system can show which documents it based its answer on, making it easy for users to verify the response.
③ Easy to keep information current — Simply add new documents to the database and they are immediately available. No model retraining is required.
Cost Benchmarks (Indicative)
Costs for RAG and internal document search AI vary considerably by implementation approach. The figures below are indicative guidelines — actual costs depend on scope, data volume, and integration requirements.
| Implementation type | Est. initial cost | Est. monthly cost | Notes |
|---|---|---|---|
| SaaS-type RAG / internal search tool | ¥0 – several hundred thousand yen | Tens of thousands to hundreds of thousands of yen | Quick to get started, but often limited in customization and integration with existing systems |
| Custom-built RAG (proprietary data, core-system integration, access control) |
Varies by scope | Varies by scope | Built to fit your workflows and existing systems; flexible data handling and access-control design |
| MRI Inc. custom development |
From ¥0 initial cost | From ¥98,000/month (excl. tax) | Custom RAG and internal search AI for SMEs, with support for existing system integration |
* Costs vary by service scope, data volume, integration targets, and development extent. The above are indicative only.
How to Get Started
Rather than aiming for a company-wide RAG deployment from day one, the key to avoiding failure is to start small, confirm results, then scale.
| STEP 1 Pick one task and one document set |
Start with the single most pressing use case — e.g., "automate internal helpdesk for HR regulations" or "automate product FAQ." Confirm the scope and volume of target documents at this stage. |
|---|---|
| STEP 2 Clean up and inventory your documents |
RAG quality depends on document quality. Removing outdated content, eliminating duplicates, and standardizing formats is the key to better retrieval accuracy. |
| STEP 3 Run a pilot (PoC) and measure results |
Test the system on your target task, and measure actual response accuracy and man-hour reduction. Use the numbers to decide: "Scale this up?" "Adjust the spec?" "Try a different process?" |
| STEP 4 Full deployment & expand to other processes |
Once PoC results confirm the value, finalize access control and security design and move to production. Then plan expansion to other business processes. |
For a checklist to avoid common AI adoption mistakes, see 7 Common AI Adoption Failures for SMEs & a PoC Checklist [2026].
harder with RAG?
Frequently Asked Questions
What is RAG?
RAG (Retrieval-Augmented Generation) is a technique that combines generative AI with a search of your internal documents, allowing the AI to answer based on your company's own information — using internal manuals and past Q&A as its source.
How is RAG different from using ChatGPT as-is?
A vanilla generative AI has no knowledge of your company's internal information, and can produce plausible-sounding but incorrect answers (hallucinations). RAG retrieves relevant internal documents before generating a response, so the AI's answers are grounded in your own data — and can cite the source.
What kinds of tasks is RAG best suited for?
RAG works well for automating internal helpdesk and inquiry responses, customer-facing FAQ, searching company regulations and manuals, and extracting key points from large document archives.
Can RAG completely eliminate hallucinations (incorrect answers)?
RAG significantly reduces hallucinations, but cannot eliminate them entirely. Response quality depends on retrieval accuracy and how well your documents are organized. For high-stakes tasks, design the system with human review as a required step.
How much does it cost to build a RAG system?
SaaS-type RAG tools typically cost from a few tens of thousands to several hundreds of thousands of yen per month (indicative). Custom-built RAG that integrates with your internal data and core systems varies by scope. MRI Inc. offers custom RAG development from ¥0 initial cost and ¥98,000/month (excl. tax). Costs vary by content and requirements.
Related articles:
・Custom Generative AI & RAG Development Cost and Timeline [2026 Guide]
・Automating Meeting Minutes & Transcription [2026]
・How to use AI safely in your company | Shadow AI risks & secure AI environments
・7 Common AI Adoption Failures for SMEs & a PoC Checklist [2026]
・SME ChatGPT Guide: How to Use It by Task & Make It Stick
* Cost and pricing information in this article is based on general market data as of June 2026 and is intended as a guideline. Effects and costs will vary by industry, scale, current state, and data situation.