- RAG lets an AI model answer using your own documents and data.
- It combines search (retrieval) with text generation.
- It reduces made-up answers and keeps information current.
- It is one of the most requested AI engineering skills.
Large language models know a lot, but they do not know your company’s policies, products or latest data. Retrieval-augmented generation (RAG) solves that by letting the model look things up before it answers.
How RAG works in 4 steps
- Prepare: split documents into chunks and convert them into embeddings.
- Store: index them in a search system or vector database.
- Retrieve: when a user asks a question, find the most relevant chunks.
- Generate: send the question plus those chunks to the model, which answers based on them.
Why companies use RAG
- Answers grounded in real company information.
- Easy to update: change the documents, not the model.
- Citations can show where an answer came from.
- Keeps sensitive data under the company’s control.
Common RAG use cases
- Internal knowledge assistants for employees.
- Customer support over help-centre articles.
- Search over contracts, policies or technical documentation.
Build your first RAG system.
View AI Engineer: Intro to RAG & Search →AI Engineer: Intro to RAG & Search is an intermediate self-paced course of 25 to 30 hours, listed from $549. See course details. Next: agentic AI and the full AI engineer learning path.
Frequently asked questions
What does RAG stand for?
RAG stands for retrieval-augmented generation: retrieving relevant information and using it to generate an answer.
Is RAG better than fine-tuning?
They solve different problems. RAG is usually best for giving a model up-to-date or private knowledge; fine-tuning changes the model’s behaviour or style.
What skills do I need to build RAG?
Python, working with APIs, basic understanding of embeddings and search, and evaluation methods.
How long does it take to learn RAG?
The AI Engineer: Intro to RAG & Search course takes about 25 to 30 hours.
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