What is RAG? Retrieval-augmented generation explained for business

Short answer

RAG (retrieval-augmented generation) is a technique where an AI system first searches your own documents for relevant information, then uses a language model to write an answer based on what it found, usually with sources. It makes AI answers more accurate and specific to your business.

Why do businesses use RAG?

General AI models don’t know your prices, policies or products, and can make things up. RAG grounds answers in your approved content, so a support bot or staff assistant gives correct, up-to-date answers.

How does RAG work?

Your documents (PDFs, web pages, policies) are split into small passages.
Each passage is converted into a numeric “embedding” and stored in a vector database.
When someone asks a question, the system finds the most relevant passages.
The AI model writes an answer using only those passages and cites them.

What can you build with RAG?

Customer support assistants on your website or WhatsApp
Internal knowledge search for HR, policies and procedures
Sales assistants that know your product catalogue
Legal and compliance research over your own documents

Is RAG secure?

It can be. Keep documents in your own database, restrict which documents each user can search, and use AI providers or private models that don’t train on your data.

Frequently asked questions

Is RAG better than fine-tuning?

For answering questions from changing company knowledge, RAG is usually cheaper and easier to keep up to date. Fine-tuning is better for teaching a model a style or format.

Which database is used for RAG?

Any vector-capable store, for example PostgreSQL with pgvector, MongoDB Atlas Vector Search, or dedicated vector databases.

Related services

Generative AI DevelopmentCustom GenAI apps for text, images, voice and video.Generative AI ConsultingPick the right GenAI models and use them safely at work.PostgreSQLReliable, AI-ready data foundations.

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