RAG stands for Retrieval Augmented Generation. If you’ve spent any time experimenting with AI, you’ve probably had moments where you thought, ‘whoa this thing feels really smart!’ And then later got a response that made you think ‘eh.. not that smart’. 

Taking a step back, let’s imagine you’re interacting with a chatGBT (😉) without any extra help. On its own, the only model knows what it was trained on and nothing else. You send it a prompt and it responds based on what it determines to be the most likely and useful answer, using probability and patterns from its training data. 

*Hey, this is a simplified explanation but walk with me* 

That’s fine, until you want a very specific answer. Say, ‘Who designed Beyonce’s Met Gala Dress in 2016?’ If the model chatGBT wasn’t trained on this data, it’d probably tell you a designer that would have most likely designed her dress but it wouldn’t be grounded in actual fact. 

So, what if it was smarter? 

That’s where RAG comes in. 

You can think of Retrieval Augmented Generation (RAG) as a hack to give an AI model additional context at the moment you ask a question. Instead of just sending the prompt, under the hood, your RAG pipeline would handle pulling additional factual context, add that to your prompt, and then the AI model would respond and likely with much more certainty.

That context might come from internal docs, recent articles, or even relevant portions of the Guinness book of world records. With RAG, the model doesn’t magically know more – it’s simply given better information to reason over before generating a response. It helps AI answer questions and respond to prompts in a way that’s more grounded, accurate, and useful.

RAG is powerful because it doesn’t try to ‘train’ an AI model (that is actually super expensive!). Instead, it focuses on giving models the right information at the moment it matters. 

For founders and product teams, that distinction is important. Not every AI feature needs a full RAG pipeline, but many benefit from better context, cleaner data, and more intentional system design. 

If you’re considering AI features like RAG, we offer a Transformation AI Reality Check – a paid working session to help teams decide what to build, what to avoid, and how AI should fit into an existing product or workflow.