Retrieval-Augmented Generation (RAG) is an architectural framework that improves the accuracy and reliability of Large Language Models (LLMs) by retrieving relevant facts from an external, authoritative knowledge base and feeding them to the model alongside the user's prompt.

While a standard LLM relies solely on its pre-trained internal knowledge (which is often outdated and prone to "hallucination"), a RAG system acts like a student taking an open-book exam. Before answering a question, it looks up the specific information needed in a trusted library (such as a company's internal wiki, legal database, or product manuals) to ground its response in reality.

The RAG process typically follows a three-step workflow:

• Retrieval: The system takes the user's query (e.g., "What is our vacation policy?") and searches a vector database to find the most relevant documents or text chunks.
• Augmentation: The retrieved information is combined with the original query to create a new, enriched prompt (e.g., "Using the following policy text: [...], answer the user's question: What is our vacation policy?").
• Generation: The LLM receives this augmented prompt and generates an answer based only on the provided context, rather than relying on its generic training data.

Strategic Impact: RAG is currently the industry standard for enterprise AI adoption. It solves the two biggest hurdles for business use: Hallucinations and Data Privacy. By forcing the model to cite its sources from the retrieved documents, organizations can verify the answers. Furthermore, because the knowledge base is external to the model, companies can update their data instantly without the expensive and time-consuming process of retraining the AI.