RAG pairs a retriever with a generator. The retriever searches an indexed corpus, often using vector similarity, to find passages related to the user's question. Those passages are inserted into the model's prompt, and the model composes a response using them. This keeps answers current and lets the system cite where information came from.
Most AI search products, including Perplexity and Google's AI answers, rely on RAG-style pipelines. That makes being retrievable a prerequisite for AI visibility. Acromatico optimizes content so it is easy to index, match, and quote, increasing the odds a RAG system pulls a client's passages into its generated answer.
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RAG lets assistants answer with current, verifiable information instead of only what a model memorized during training. Because it retrieves live documents and can cite them, RAG powers most AI answer engines and determines which sources get surfaced in a response.
Make content easy to index and match: write clear, self-contained passages, use descriptive headings, keep facts accurate and current, and reinforce entity clarity. The more precisely a retriever can match your passage to a query, the likelier it enters the generated answer.
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