Retrieval-augmented generation

Retrieval-augmented generation (RAG) is a technique that fetches brand-specific context — past posts, brand guidelines, performance data — and injects it into the LLM prompt automatically before each generation.

Without RAG, an LLM only knows what is in its training data plus what the user types into the prompt. RAG closes the gap by attaching a retrieval layer: when the user asks for a draft, the system searches the brand’s corpus, picks the most relevant pieces (often by vector similarity), and prepends them to the prompt as context. The model then generates with the brand-specific signal in scope.

In marketing tools, RAG is what makes the difference between "describe your brand voice in 500 characters" and "the system already knows how you sound". The retrieval can be over voice samples, past posts on the same topic, performance data, or pinned strategic insights — whatever signal the generator needs that wouldn’t fit in a static prompt.

RAG is the architectural reason a tool that has read your last 100 posts produces better output than a tool that hasn’t. The depth of the retrieval layer is usually the difference between AI content you ship and AI content you have to rewrite.