RAG
In simple words
Give the model the right documents at query time instead of hoping it memorised them.
The fuller explanation
A language model knows what was in its training data, up to a cutoff, with no citations and no awareness of your organisation. Retrieval-augmented generation fixes that by retrieving relevant documents at query time and placing them in the context window before the model answers.
The loop is short: embed the query, search a corpus for the closest passages, insert them into the prompt, generate an answer grounded in them. Four steps, and the naive version can be built in an afternoon.
Which is exactly the trap. The demo works immediately and the production system does not, because quality is dominated by retrieval rather than generation. If the right passage is not in the top results, no model can save the answer. Nearly every child node below this one - chunking, hybrid search, reranking, evaluation - exists to address a specific way that retrieval quietly fails.
Learn these first
Real prerequisites, taken from the map rather than guessed.
Sources
Where this came from, so you can go past us.
Where does this sit on your route?
The free assessment places you on the same map and names which terms stand between you and the role you want.
Take the free assessmentSee it in context
The Atlas shows this term with everything that leads into it and everything that follows, as one picture.
Open the map