In 30 seconds

RAG lets an AI look up relevant information before it answers, instead of relying only on what the model already contains. It is how you get an AI to answer from your company's own documents.

It helps the model find information. It does not guarantee the information is correct, current or well interpreted.

ELI5

Imagine an employee answering a policy question. Instead of relying on memory, they first find the relevant pages in the current handbook, read them, and then answer using those pages.

A RAG system does something similar. It searches a knowledge source, selects the passages that seem relevant, and gives those passages to the model together with the question. The letters stand for retrieval-augmented generation: retrieve first, then generate the answer.

Why you would care

Models do not reliably know your company's specifics: your prices, your policies, your product details. And that information changes. RAG lets the AI answer from your own, current sources rather than from whatever it happened to absorb during training.

This is what turns a general assistant into one that can answer questions about your actual business.

What happens, roughly

QUESTION
   ↓
SEARCH your sources for relevant passages
   ↓
SELECT the most relevant pieces
   ↓
GIVE those pieces to the model with the question
   ↓
MODEL ANSWERS using them

Under the hood, systems often split documents into smaller chunks, make them searchable by meaning using embeddings and semantic search, and add the best matches to the model's context. You only need that deeper layer when you design the retrieval system itself.

What RAG does not solve

Giving an AI more documents does not automatically make its answers better. RAG does not guarantee that:

  • the source is correct
  • the source is current
  • the right passage was retrieved
  • the answer follows company policy
  • the model interpreted the passage correctly

If two documents contradict each other, the system may retrieve the wrong one, or blend outdated and current information into a confident, wrong answer. Preparing and owning the sources matters as much as the retrieval itself.

When you may not need it

If the model only needs information already provided directly in the workflow, or you have a handful of documents a person can easily point to, retrieval may add complexity you do not need yet.