Imagine you’re a financial analyst tasked with comparing the GDP of France and Italy over the last five years. You query a language model, asking: “What are the current GDP figures of France and Italy, and how have they changed over the last five years?” Using Retrieval-Augmented Generation (RAG), the model first retrieves relevant information from external sources, then generates this response: “France’s current GDP is approximately $2.9 trillion, while Italy’s is around $2.1 trillion. Over the past five years, France’s GDP has grown by an average of 1.5%, whereas Italy’s GDP has seen slower growth, averaging just 0.6%.” In this case, RAG improves the model’s accuracy by incorporating real-world data through a single retrieval step. While effective, this method can struggle with more complex queries that require multiple, dynamic pieces of real-time data. Enter Retrieval Interleaved Generation (RIG)! Now, you submit a more complex query: “What are the GDP growth rates of France and Italy in the past five years, and how do these compare to their employment rates during the same period?” With RIG, the model generates a partial response, drawing from its internal knowledge about GDP. However, it simultaneously retrieves relevant employment data in real time. For example: “France’s current GDP is $2.9 trillion, and Italy’s is $2.1 trillion. Over the past five years, France’s GDP has grown at an average rate of 1.5%, while Italy’s growth has been slower at 0.6%. Meanwhile, France’s employment rate increased by 2%, and Italy’s employment rate rose slightly by 0.5%.” Here’s what happened: RIG allowed the model to interleave data retrieval with response generation, ensuring the information is up-to-date and comprehensive. It fetched employment statistics while continuing to generate GDP figures, ensuring the final output was both accurate and complete for a multi-faceted query. What is Retrieval Interleaved Generation (RIG)? RIG is an advanced technique that integrates real-time data retrieval into the process of generating responses. Unlike RAG, which retrieves information once before generating the response, RIG continuously alternates between generating text and querying external data sources. This ensures each piece of the response is dynamically grounded in the most accurate, up-to-date information. How RIG Works: For example, when asked for GDP figures of two countries, RIG first retrieves one country’s data while generating an initial response and simultaneously fetches the second country’s data for a complete comparison. Why Use RIG? Real-World Applications of RIG RIG’s versatility makes it ideal for handling complex, real-time data across various sectors, such as: Challenges of RIG While promising, RIG faces a few challenges: As AI evolves, RIG is poised to become a foundational tool for complex, data-driven tasks, empowering industries with more accurate, real-time insights for decision-making. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more