RAG (Retrieval-Augmented Generation)

RAG is a technique where an AI model retrieves relevant information from your own documents before generating an answer, rather than relying only on what it learned during training.

Many assume that getting AI to understand company data means training a proprietary model first, often months of data collection and labeling — in most cases it's enough to have internal documents, FAQs, and manuals, and let RAG retrieve from them before answering. No training from scratch is needed, and updating knowledge is as simple as swapping a document, which keeps maintenance far cheaper. Picture a food factory rolling out a support bot: feed it the SOPs, common complaint procedures, and return policies, and the agent can retrieve and answer on the spot, without waiting on a data science team to write code — updating the source documents quarterly is often enough to keep answers current. On cost, RAG-related development and knowledge-base setup typically account for 35-45% of an AI agent project's budget, the single largest cost line and the part most worth scrutinizing at acceptance. A common confusion is treating RAG and fine-tuning as interchangeable: RAG solves 'the model doesn't know your data,' while a problem like 'the model's tone or judgment logic needs adjusting' belongs to fine-tuning or prompt engineering instead — different layers of the problem that shouldn't be expected to substitute for each other.