Retrieval-Augmented Generation (RAG) retrieves relevant material from an external repository or the web when a question is asked, then includes that material in the prompt instead of relying solely on a model’s pretrained knowledge. It can incorporate information beyond the model’s cutoff and private internal documents that were never present in training, helping reduce hallucinations. Performance ultimately depends on retrieving genuinely relevant documents, so retrieval and reranking quality are critical. Chunking strategy and embedding-model selection are key variables influencing overall answer quality.
Some websites are seeing sharp increases in site: operator queries in Search Console performance reports. Explore possible causes, measurement effects and diagnostic responses.
