RAG (Retrieval-Augmented Generation)
A pattern that fetches relevant sources before the model answers, grounding generation in real documents.
What is RAG?
Retrieval-Augmented Generation (RAG) is a pattern where an AI engine retrieves relevant documents first, then generates its answer grounded in those sources. It reduces hallucination and makes citations possible.
RAG is the bridge between your content and an AI answer: the retrieval step is exactly where your brand is selected — or skipped.
In a RAG pipeline the user query is embedded and matched against a corpus, the top passages are pulled, and the model is asked to answer using them. The generated reply can then cite the retrieved sources. This is how web-grounded engines like Perplexity, and the grounded modes of ChatGPT and Gemini, point back to real URLs.
RAG changes the optimisation target. To be retrieved, content must be semantically close to how buyers phrase the question, cleanly chunked, and unambiguous about the entity it describes. Answer-first structure, explicit definitions and structured data all raise the odds your passage is the one selected and quoted.
Inside SkuLift, a per-workspace knowledge base is itself a RAG corpus: documents are vectorised and indexed, then feed the AEO-GEO Strategist and KB-derived pillar recommendations — the same retrieval mechanics, turned inward to plan what to publish.