A client calls your insurance brokerage and asks about their policy's water damage coverage. Without RAG, the AI agent would give a generic answer about water damage coverage from its training data, which might be wrong for that specific policy. With RAG, the agent retrieves that client's actual policy document, reads the relevant endorsements, and provides an answer that reflects their specific coverage limits, deductibles, and exclusions.
AWS defines RAG as "the process of optimizing the output of a large language model so it references an authoritative knowledge base outside of its training data before generating a response." IBM Research explains that RAG extends LLM capabilities "to specific domains or an organization's internal knowledge base, all without the need to retrain the model."
Here is the problem RAG solves. Large language models are trained on public internet data. They know general information about accounting, law, real estate, insurance, and most other fields. But they do not know your company's pricing, your internal processes, your client history, or your specific policies. When a business deploys AI without RAG, the AI generates plausible-sounding but potentially inaccurate responses because it is guessing based on general knowledge.
RAG works in three steps. First, the system converts your business documents (contracts, policies, procedures, FAQs, knowledge base articles) into a searchable format. Second, when a question comes in, the system searches your documents for the most relevant information. Third, the AI generates its response using that retrieved information as context, ensuring the answer reflects your actual data.
For business owners, RAG is what makes the difference between an AI that sounds smart and an AI that is actually useful. A real estate brokerage using RAG can have an AI agent that answers questions about specific listings, neighborhood data, and transaction history accurately. A law firm using RAG can have an AI that references the correct statute when a potential client describes their situation. An accounting firm using RAG can have an AI that knows the firm's specific engagement letter templates and fee schedules.
RAG also addresses the hallucination problem. Because the AI is grounding its responses in your actual documents rather than generating answers from memory, the risk of fabricated information drops significantly. The AI can cite the specific document or policy it based its answer on, giving both your team and your clients confidence in the response.
Every AI business engine that DeployLabs builds uses RAG to ensure agents work with your actual business data. For more on how this fits into a complete AI system, see our comparison of AI approaches.