A typical RAG model first uses semantic search to find the most relevant content before generating a response based on that content. This structure not only improves accuracy but also makes it easier to trace answers back to their source.
RAG is often used with large language models (LLMs), which are AI systems trained to understand and generate human-like text. While a traditional LLM generates answers based only on what it learned during training, RAG adds a retrieval layer that allows the model to “read” relevant contextual content as it forms a response. This technique, known as “context engineering,” helps reduce hallucinations and improves the reliability of outputs.