← Glossary · AI Term

RAG (Retrieval-Augmented Generation)

RAG is a technique where an AI assistant first looks up relevant information from your business documents (SOPs, FAQs, product catalogs) before answering, so its responses are grounded in YOUR specific data rather than generic training. It is how 'Custom AI Trained on Your Business' actually works.

The longer version

Without RAG, an AI assistant answers from its training data, which has nothing specific to your business. With RAG, you upload your operating procedures, policies, product catalogs, and internal docs into a vector database. When someone asks a question, the AI first retrieves the most relevant chunks from your docs, then generates an answer using both that context and its general intelligence. The result: answers that are accurate to YOUR business, not generic. Used for internal staff Q&A systems and customer-facing chatbots.

Common questions

Is RAG expensive to set up?

Moderate complexity. For most SMBs, $199-$299/mo as part of a Scale-tier plan covers setup + ongoing maintenance.

How much data does it need?

Even 5-20 PDF documents (SOPs, manuals, FAQs) is enough for useful RAG systems. More data + more polish = better results.

Can RAG hallucinate?

Less than non-RAG systems, but yes occasionally. We always test thoroughly and route uncertain cases to humans.

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