Short answer: RAG (Retrieval-Augmented Generation) is a method in which an AI model first finds the relevant sections in your company's documents and then answers the question based on them. The model answers from your content rather than its general knowledge, can show which document it relied on, and does not need retraining when your documents are updated.
How does RAG work?
- Preparation: Documents such as PDFs, Word files, wiki pages and email templates are split into small chunks and added to a searchable index.
- Retrieval: When a user asks a question, the system finds the chunks most relevant to it in meaning.
- Answer: The retrieved chunks are passed to the language model together with the question; the model writes its answer based solely on this information.
- Source: Below the answer, the system shows which section of which document was used; if the user wants to verify it, they can click to open it.
RAG or model training (fine-tuning)?
| Topic | RAG | Fine-tuning |
|---|---|---|
| Purpose | Answers based on up-to-date company information | Teaching the model a specific style or task |
| Updating information | Changes take effect as soon as a document is updated | Requires retraining |
| Citing sources | Naturally possible | Difficult |
| Initial cost | Relatively low | Higher |
| Best suited for | Procedures, products, contracts, support information | Fixed formats, classification, specialized terminology |
For most businesses, RAG is the right starting point. Fine-tuning is considered as a second step only when consistency is required on a specific task.
Where is it used in businesses?
- Internal support: Instantly answering employee questions such as "How does the leave process work?" or "What are the warranty terms for this product?"
- Sales and quotes: Fast information based on the product catalog and pricing policy
- Customer support: Grounding the support team's draft replies in company documentation
- Compliance and quality: Finding the relevant clause in procedures and regulatory documents
What does a well-functioning RAG system need?
- Document quality: Contradictory or outdated documents lead to wrong answers. Before you start, decide which document is the "current version."
- Access control: Each employee should only get answers from documents they are authorized to see; accounting documents should not be visible to the sales team.
- "I don't know" behavior: If the answer is not in the documents, the system should say so clearly instead of making something up.
- Measurement: A test set built from real questions is prepared, and the accuracy of answers is checked regularly.
- Data location: Which server stores the documents and searches should be clarified from the outset, in terms of both KVKK (Turkey's Personal Data Protection Law, Law No. 6698) and company policy.
How do you start a RAG project?
The best starting point is a single group of documents and a single user audience; for example, only human resources procedures and only employees. In this pilot, accuracy and usage are measured, and new document groups are added afterward. For the overall framework, see our article AI for Business: Where to Start?
Is your company knowledge getting lost in folders? With our AI solutions, we build an assistant that relies on your documents, cites its sources and follows your access rules.
Sources
Frequently Asked Questions
The error rate drops significantly but does not reach zero. That is why source citation, "I don't know" behavior and regular accuracy tests should be used together.
It depends on the setup. The search index can be kept on your own server; only short sections relevant to the question are sent to the language model. If needed, a model running on your own server can also be used.
PDF, Word, Excel, web pages, wiki content and plain text are the most common. Scanned documents must first be converted to text.