In short
RAG lets an AI assistant answer from your own documents instead of guessing. Learn how retrieval-augmented generation works and where businesses use it.
Large language models (LLMs) are good at writing, but they do not know your company’s policies, products or latest prices. Retrieval-augmented generation — RAG — fixes that.
How RAG works
- Index – your documents are split into small passages and stored in a searchable index (often a vector database).
- Retrieve – when a user asks a question, the system finds the most relevant passages.
- Generate – the LLM writes an answer using only those passages and can cite them.
Why businesses use RAG
- Answers are based on your own, up-to-date content
- Fewer made-up answers
- No need to retrain a model when documents change
Common use cases
- Customer support assistants
- Internal knowledge bases for staff
- Searching contracts, manuals and policies
Conclusion
RAG is the most practical way to make AI useful with your own information. Good results depend on clean documents and well-tested retrieval.
Frequently asked questions
Is RAG the same as fine-tuning?
No. Fine-tuning changes the model itself; RAG keeps the model as it is and gives it the right information at question time. Many projects start with RAG.
Is my data safe with RAG?
It can be. The index can be hosted privately and access can follow your existing user permissions.
