Couchbase: Real-time Data will become the Standard for Businesses


VP of Product and Strategy at Couchbase Rahul Pradhan shared his prediction in 2024. According to him,  retrieval-Augmented Generation will be paramount for grounded, contextual outputs when leveraging AI. The excitement around large language models and their generative capabilities will continue to bring with it a problematic phenomenon of model hallucinations. These are instances when models produce outputs that, though coherent, might be detached from factual reality or the input’s context. As modern enterprises move forward, it’ll be important to demystify AI hallucinations and implement an emerging technique called Retrieval-Augmented Generation (RAG) that when coupled with real-time contextual data can reduce these hallucinations, improving the accuracy and the value of the model. RAG brings in context about the business or the user, reducing hallucinations and increasing truthfulness and usefulness.

“Real-time data will become the standard for businesses to power generative experiences with AI. Data layers should support both transactional and real-time analytics,” he said.

“The explosive growth of generative AI in 2023 will continue strong into 2024. Even more enterprises will integrate generative AI to power real-time data applications and create dynamic and adaptive AI-powered solutions​. As AI becomes business-critical, organizations need to ensure the data underpinning AI models is grounded in truth and reality by leveraging data that is as fresh as possible. Just like food, gift cards and medicine, data also has an expiration date. For generative AI to truly be effective, accurate and provide contextually relevant results, it must be built on real-time, continually updated data. The growing appetite for real-time insights will drive the adoption of technologies that enable real-time data processing and analytics. In 2024 and beyond, businesses will increasingly leverage a data layer that supports both transactional and real-time analytics to make timely decisions and respond to market dynamics instantaneously.”

Expect a paradigm shift from model-centric to data-centric AI. He explains that data is key in modern-day machine learning, but it needs to be addressed and handled properly in AI projects. Because today’s AI takes a model-centric approach, hundreds of hours are wasted on tuning a model built on low-quality data.

“As AI models mature, evolve and increase, the focus will shift to bringing models closer to the data rather than the other way around. Data-centric AI will enable organizations to deliver both generative and predictive experiences that are grounded in the freshest data. This will significantly improve the output of the models while reducing hallucinations.”

Another trend is businesses will tap into AI copilots for faster time to insights.  The integration of AI and machine learning within data management processes and analytics tools will continue to evolve. As generative AI technology emerges, businesses need a way to interact with AI and the data it produces at a contextual level. Leveraging augmented data and analytics, businesses will start to build AI copilots into their products to achieve faster time to insights. With the ability to understand and process large amounts of data, copilots act as assistants to AI models to sort through data and generate best practices and recommendations. Data augmentation is a powerful tool that will change the way businesses are building infrastructure and applications in the coming years, as augmented data management will automate routine data quality and data integration tasks, while augmented analytics will provide advanced insights and automate data-driven decision-making.

One of the most exciting trends for 2024 will be the rise of multimodal LLMs. With this emergence, the need for multimodal databases that can store, manage and allow efficient querying across diverse data types has grown. However, the size and complexity of multimodal datasets pose a challenge for traditional databases, which are typically designed to store and query a single type of data, such as text or images. Multimodal databases, on the other hand, are much more versatile and powerful. They represent a natural progression in the evolution of LLMs to incorporate the different aspects of processing and understanding information using multiple modalities such as text, images, audio and video. There will be a number of use cases and industries that will benefit directly from the multimodal approach including healthcare, robotics, e-commerce, education, retail and gaming. Multimodal databases will see significant growth and investments in 2024 and beyond — so businesses can continue to drive AI-powered applications.

The convergence of AI and edge computing will continue to mature, allowing for more robust real-time analytics and decision-making at the edge. Enhanced edge AI capabilities will reduce the need for data transmission to the central locations in the cloud, ensuring faster responses and better privacy preservation. As the benefits of Edge AI and Inferencing closer to the application and data become evident, organizations will start looking into various edge inferring stacks and databases in order to process the data locally. This distributed inferencing allows models to be trained across multiple devices or servers holding local data samples, without exchanging them and addressing data privacy and compliance concerns.

“This, combined with Edge AI, will enable efficient data processing on local devices, reducing latency and ensuring data privacy,” he closed.