Retrieval-Augmented Generation (RAG) is a technique that combines traditional information retrieval with generative AI models. Instead of relying solely on the model’s training data, RAG first pulls in relevant external content (like documents, web pages, or database entries) and then uses that real-world context to generate more accurate, grounded responses.
In practice, this means the AI “retrieves” relevant information before it “generates” an answer – resulting in responses that are more trustworthy, up-to-date, and aligned with a specific dataset.
SearchRovr uses a RAG-like approach to provide intelligent, on-site answers that are grounded in your actual WordPress content. This ensures that the AI isn’t hallucinating answers but instead referencing your own published material – like blog posts, product descriptions, or help docs – when responding to users.