RAG Begins With Finding Something Relevant
This Short introduces retrieval-augmented generation through a simple connection: give an AI a search step before asking it to answer.
Instead of expecting the model to already contain the information I need, a retrieval system looks for relevant material and supplies it as context.
That makes the search result part of the answer's foundation. If the system finds the wrong passage, a fluent response can still miss the point.
I would inspect a small example in three stages: the question, the passages retrieved, and the answer built from them. Does each passage help? Can I trace the important claims back to it?
RAG is useful to understand because it makes the source an explicit part of the workflow. It still needs checking. Finding some context is not the same as finding the right context.