A concrete example
You ask a question about an internal procedure. The system finds the relevant passages, then hands them to the model to answer.
Why it matters
It's one of the most practical ways to cut down on made-up answers and put private data to use.
You run into it in documentation chatbots, internal knowledge bases, and agents that cite their sources.
Don't mix it up with
Embedding: An embedding turns a piece of text into a list of numbers so you can compare its meaning with other texts.
Vector database: A vector database stores embeddings and finds the texts closest to a question.
Common mistakes
- Assuming RAG guarantees a true answer.
- Splitting the documents badly.
- Not showing the sources used.
Quick checklist
- First I check whether the word names a concept, a tool, a risk, or a metric.
- I tie it to a concrete case: You ask a question about an internal procedure. The system finds the relevant passages, then hands them to the model to answer.
- I keep the main trap in mind: Assuming RAG guarantees a true answer.
Quick questions
What is RAG in AI?
RAG lets an AI answer using documents pulled up at the moment you ask the question.
Where will I run into RAG?
You run into it in documentation chatbots, internal knowledge bases, and agents that cite their sources.
Which word should I read next?
Start with Embedding, Vector database, Chunking.