What Is RAG? A Founder's Guide to Adding AI to Your Product

RAG - retrieval-augmented generation - is one of the most useful ways to add AI to a product, and one of the most misunderstood. If you want an AI feature that answers questions using your own data (your docs, your catalogue, your knowledge base), RAG is usually how it's done. Here's the plain-English version.

Why not just use ChatGPT?

A general AI model only knows what it was trained on. It doesn't know your internal documents, your product details, or anything that happened after its training cut-off - and if you ask anyway, it may confidently make things up. RAG fixes this by feeding the model the relevant information from your data at the moment of the question.

How RAG works (simply)

• Your content is split up and converted into embeddings - numerical representations of meaning.
• Those are stored in a vector database.
• When a user asks something, the system retrieves the most relevant pieces.
• Those pieces are handed to the AI model, which answers using them - grounded in your data.

When your product needs RAG

• A support or docs assistant that answers from your help content.
• Search over a large internal knowledge base.
• A product that answers questions about a user's own data or files.
• Anywhere accuracy and "no made-up answers" matter.

When you don't

If your AI feature is generic - drafting text, summarising, brainstorming - you may not need RAG at all. Don't add complexity you don't need. The right question is always "does this need to know our specific data?"

Thinking about an AI feature and not sure if RAG is the right fit? Book a free consultation and I'll help you scope it - including whether you need it at all.