02 / AI BROS. LEARN
RAG
AI that looks up relevant information before it answers, so it can work from your sources instead of memory alone.
LESSON 02THE THREE-WAY EXPLAINER
What is RAG?
PLAIN ENGLISH VERSION
RAG is AI with permission to look things up before it answers.
Instead of relying only on what it learned during training, the system searches a set of sources—like your documents, a website, or a database—pulls the most relevant pieces, and gives them to the AI alongside your question.
The AI then writes its answer using that material. It is the difference between answering from memory and opening the right binder first.
PUT IT TO WORK
WHY SHOULD I CARE?
RAG is useful when an answer depends on information the AI was never trained on, changes often, or should be traceable to a trusted source.
- UPGRADE YOURSELF Find the right detail across travel plans, receipts, or household files
- UPGRADE YOURSELF Ask questions across your own notes without rereading every page
- ACCELERATE THE BUSINESS Give sales teams current product, pricing, and policy answers
- ACCELERATE THE BUSINESS Prepare account briefs from CRM notes, emails, and meeting history
- REINVENT THE ENTERPRISE Troubleshoot factory equipment using manuals and maintenance records
- REINVENT THE ENTERPRISE Ground regulated decisions in the approved policy for that customer and moment
The retrieval play
What happens in a RAG play?
THE QUESTION
A person asks for something specific.
THE PLAYBOOK
Trusted documents, records, pages, or data are available to search.
THE RETRIEVER
Search finds the passages most likely to help.
THE HUDDLE
Those passages join the question inside the model's context.
THE ANSWER
The model responds using the retrieved material and its existing abilities.
TRY IT YOURSELF
Run a mini RAG play.
Bring one trusted document into the huddle and ask AI to find the evidence before it answers.