Retrieval-Augmented Generation (RAG) is the underlying technique that powers most "AI that knows your company's knowledge base" products. You probably already use RAG without knowing it. Here is what it actually is and when it matters for business buyers.
RAG is a technique where AI models retrieve relevant documents from a knowledge source BEFORE generating an answer. Instead of the model relying only on its training data, it pulls in your specific documents to ground its response.
In practice: when you ask Claude or ChatGPT a question about your company docs (loaded into a Project or GPT), it uses RAG to find the relevant doc snippets and then writes an answer grounded in them.
Custom knowledge without retraining. You do not have to fine-tune a model. Just load your docs.
Citations. Good RAG implementations cite which document the answer came from, so users can verify.
Easy to update. Change a doc, the RAG system uses the new version next time.
Affordable. Loading docs into a Claude Project costs $0 incremental. Building a custom RAG stack is $20K-$100K.
Use built-in (Claude Projects, ChatGPT custom GPTs): For most B2B businesses under 250 employees. Loads 50-200 docs, gives 90% of the value of custom builds at 5% of the cost.
Build custom RAG: When you need to query thousands of documents, integrate with proprietary data systems, or have specific compliance requirements that off-the-shelf tools cannot meet.