Imagine asking a computer a complex question and getting an accurate, factual answer, even if that information wasn't explicitly programmed into it. That's the promise of Retrieval-Augmented Generation (RAG), a cutting-edge technique in AI that allows Large Language Models (LLMs) to access and process external knowledge sources. But there's a catch: traditional RAG systems rely on dense retrieval, a method that needs extensive labeled data to effectively find relevant information. This data is often scarce and expensive to create, limiting the potential of RAG. Enter W-RAG, a novel approach that uses the LLMs themselves to solve this data scarcity problem. Instead of relying on manually labeled datasets, W-RAG leverages the power of LLMs to create 'weakly labeled' training data for dense retrievers. How does it work? Imagine the LLM as a highly skilled librarian. It takes a question, scans a vast collection of information, and ranks the passages based on how likely they are to contain the correct answer. This ranking, based on the LLM's ability to reason and connect questions to potential answers, provides valuable training signals for the dense retriever. The result? A more efficient and effective way to train dense retrievers, boosting the overall performance of RAG systems. Tested on four major OpenQA datasets, W-RAG significantly improved both retrieval and question-answering accuracy. This innovative approach opens exciting new possibilities for OpenQA, bringing us closer to AI systems that can truly understand and answer complex questions. This advance not only enhances accuracy but also paves the way for more robust and efficient question-answering systems, capable of tackling even the most challenging knowledge-intensive tasks. While challenges remain, such as optimizing for different passage types and mitigating potential biases, W-RAG offers a promising glimpse into the future of AI-powered knowledge access.