RAG

A technique that enables a language model (LLM) to respond using your company’s documents, not just its general training. The system first searches a proprietary knowledge base for relevant passages, then the LLM generates a response by combining what it found with its language capabilities. It’s the most common way to bring generative AI into corporate settings without retraining a model.

When should I use RAG at a company?

When you need answers based on internal policies, manuals, contracts, historical records, or any knowledge base the base model doesn’t know. Typically the first step after prompting and before considering fine-tuning.

When is it NOT a good idea RAG?

When your data changes by the second, when you need complex quantitative reasoning, or when source data quality is poor—RAG amplifies the quality of the corpus; it does not make up for it.