AI Glossary
What Is RAG? A Plain Explanation for Executives
RAG (Retrieval-Augmented Generation) is a method where AI first searches a document library for relevant information, then writes its answer from what it found. Answers are based on your organization's real data and can cite their sources, instead of relying on the model's general memory.
What are the steps to request a product exchange?
Found the relevant documents. Here is a summary of the steps per the latest policy.
Source: Product exchange policyWhat is RAG?
RAG stands for Retrieval-Augmented Generation. In plain terms, it means writing an answer that is supported by retrieved information.
Think of an employee who opens the right manual or file before answering a customer, instead of answering from memory alone.
A general AI model only knows what it was trained on. It does not know your company's prices, products, or internal policies. RAG fills that gap by having the AI read your organization's data every time it answers.
How does RAG work?
Store in the library
Company documents are loaded, split into small parts, and indexed so they can be searched.
Search by meaning
When someone asks a question, the system finds relevant content by meaning, not just by matching words.
Answer with sources
The AI writes an answer from what it found and attaches the original documents so people can check.
How is RAG different from fine-tuning?
| Topic | RAG | Fine-tuning |
|---|---|---|
| Principle | Searches for information before every answer | Trains the model further on a set of data |
| When information changes | Update the documents in the library | Usually needs retraining |
| Cites sources | Yes, because it knows which document the content came from | Hard to do |
| Best for | Business knowledge that changes often, such as prices, policies, and manuals | Adjusting writing style or a specific work format |
How does Phak AI use RAG?
Phak AI uses RAG at the core of its AI assistant. It answers only from your organization's knowledge, not the open internet, and can point to the original document behind every answer.
The library supports PDF, Word, Excel, images, video, web links, and YouTube. If the system is not confident, it does not guess. It hands the question to a person. Your data is not used to train AI models.
See it in use at Knowledge base.
Frequently asked questions
Does RAG mean the AI is never wrong?
There is no guarantee, but it lowers the risk a great deal. Answers are based on real documents and cite their sources, so people can check them.
Does RAG work with Thai-language documents?
Yes. Phak AI is designed for Thai organizations, so people can ask questions in Thai and get answers from the organization's documents.
How should we prepare our data before using RAG?
Start with the documents your team really uses and the questions they ask often. The more complete and current the documents, the better the answers.
Read next
AI glossary
AI terms executives should know, in plain language, with examples from real organizations
Read more →Central Knowledge Base
All your documents in one place. Ask in Thai and get answers that cite the original document.
Read more →Platform
The organization's central AI brain: knowledge in one place, ask in Thai, answers cite sources
Read more →Company Second Brain
A central system that keeps company knowledge when people leave and answers questions in plain language
Read more →Talk to the Phak AI team
Tell us about your business. The Phak AI team will show you the systems that fit and plan the rollout with you, without replacing what you have.