Retrieval-Augmented Generation (RAG)

RAG sounds technical. It is not -- at least not in the way you need to understand it as a marketing leader.

Here is the problem RAG solves. Standard AI models like ChatGPT are trained on public internet data. They know a lot about the world in general, but they know nothing about your company specifically. They do not know your product names, your pricing tiers, your brand voice, your competitive positioning, or what your CEO said at the last all-hands. When you ask a generic AI to write about your product, it fills in the gaps with plausible-sounding guesses. In marketing, that is called making things up.

RAG fixes this by adding a retrieval step before generation. Before the AI writes anything, it first searches through a database of your documents -- product specs, brand guidelines, case studies, internal wikis, previous campaigns -- and pulls in the relevant pieces. Then it generates a response using that retrieved context. The result is output that references your actual data instead of hallucinating it.

For marketing teams, RAG is the difference between an AI tool that produces generic content and one that produces on-brand, factually accurate content. Tools like Jasper (with its Knowledge Base feature) and Writer use RAG under the hood. When you upload your style guide and product documentation to these platforms, you are building the retrieval database that makes RAG work.

The practical implication for CMOs: when evaluating AI vendors, ask whether they support RAG or custom knowledge bases. If a vendor cannot ingest your company's proprietary data, every piece of content it generates will need manual fact-checking against your actual product details. That editing time erodes the productivity gains that justified the AI investment in the first place.