What is Retrieval-augmented generation (RAG)?

Retrieval-augmented generation (RAG) is a pattern where an AI model is given relevant external information at query time — fetched from a vector database, a document store, a search index, or an API — and conditions its answer on that retrieved context rather than relying purely on what it learned during training. For marketing applications, RAG is the substrate behind 'AI assistant trained on our brand bible and past campaigns': the assistant doesn't actually fine-tune on your data; instead, every question retrieves the relevant policy, brief, or asset and feeds it into the model as context. RAG matters in 2026 for three reasons: it lets generic foundation models behave as if they know your private brand context (without exposing that context to training pipelines); it gives the AI a citation trail back to source documents, which is the foundation of trustworthy enterprise AI; and it sidesteps the hallucination problem on facts that exist in retrievable form (product specs, pricing, policy, prior-campaign performance). The pattern competes with fine-tuning for the 'teach the model about us' use case; in mid-2026, RAG wins for most marketing applications because it's cheaper, updates instantly when source documents change, and provides built-in source attribution that fine-tuned models cannot match.