Definition: Architecture that combines information retrieval from external sources with LLM text generation for more accurate responses.
— Source: NERVICO, Product Development Consultancy
What is RAG
RAG (Retrieval-Augmented Generation) is an architecture that combines information retrieval from external sources with the text generation capability of an LLM. Instead of relying exclusively on knowledge stored in the model’s weights, RAG queries databases, documents, or APIs to obtain up-to-date and relevant information before generating a response. This reduces hallucinations and enables the model to access data that did not exist during its training.
How It Works
The RAG flow has three stages. First, the user’s query is converted into a vector embedding and used to search for the most relevant fragments in a vector database. Second, the retrieved fragments are inserted into the prompt along with the original question, providing factual context to the LLM. Third, the model generates a response grounded in the retrieved information. System quality depends directly on retrieval precision: if the retrieved fragments are not relevant, the response will be poor regardless of the model’s capability.
Why It Matters
RAG solves two critical LLM limitations: outdated knowledge and hallucinations. For businesses, this means being able to build AI assistants that respond with up-to-date and verifiable information, citing specific sources. It is the preferred architecture for enterprise chatbots, technical support systems, and intelligent search tools over internal documentation.
Practical Example
An e-commerce company implements RAG for its customer service assistant. The system indexes the product catalog, return policies, and order history in a vector database. When a customer asks about their order status, the system retrieves the relevant information and generates a personalized, accurate response rather than a generic one.