The text discusses advanced retrieval-augmented generation (RAG) strategies for building more robust and accurate RAG applications. These strategies are necessary due to the limitations of basic vector similarity search, which only compares semantic content without regard for other data aspects. The article introduces a step-back approach to prompting, where the model first asks and answers a general question about the fundamental principle or concept behind a query, and parent document retrievers, which use larger documents as context while indexing smaller chunks of those documents for better representation. Additionally, it discusses available strategies such as typical RAG, parent retriever, hypothetical questions, and summaries. The article also provides guidance on implementing these advanced RAG strategies using the neo4j-advanced-rag template with LangChain templates, which can be used to deploy retrieval-augmented generation applications in just a few minutes. By following the steps outlined in the article, developers can build more accurate and contextual RAG applications for better user experiences.