Imagine a world where search engines not only find documents but also directly answer your questions, pulling together information seamlessly from multiple sources. That's the promise of RICHES, a novel approach from Google DeepMind that revolutionizes how we think about retrieval. RICHES, short for Retrieval Interlaced with Sequence Generation, challenges the conventional Retrieve-then-Generate paradigm by intertwining the two processes within a single language model. Forget complex pipelines with separate retriever and generator components—RICHES streamlines the entire process into a unified system. This innovative approach leverages the strengths of large language models (LLMs) as knowledge warehouses, using their ability to generate text as a search mechanism. By directly decoding document content or related keys, RICHES pinpoints relevant information within a vast corpus. This is not simply matching keywords; the system understands context, piecing together information from various sources much like a human researcher. The system shines in complex, multi-hop question answering scenarios where traditional search engines often stumble. It can not only answer questions that require pulling information from multiple documents but also provide attributed evidence for its answers, increasing transparency and trustworthiness. The magic of RICHES also lies in its flexibility. By using prompts, it adapts to diverse tasks without any further training, unlike traditional pipelines that often require tedious retraining for every new task. The implications for search and question answering are far-reaching. Imagine search engines that provide not just a list of links but also a concise, well-reasoned answer supported by direct evidence. Or think of chatbots that can fluidly integrate information from your personal documents, providing contextually relevant responses. However, the approach also presents some challenges. While RICHES excels at precision, scaling to retrieve dozens of documents, a requirement for some summarization tasks, presents hurdles. The processing of long documents with diffused information also poses a challenge. Despite these limitations, RICHES represents a bold step forward. It streamlines the search process, enhances context understanding, and empowers LLMs to not only retrieve but also generate insightful answers. This innovative approach paves the way for the next generation of search engines and AI assistants, offering a glimpse into the future of information access.