Accordingly Research by IBM®, about 42 percent of corporations surveyed use AI of their business. Of all of the use cases, lots of us are actually very aware of AI chatbots using natural language processing that may answer our questions and help with tasks like writing emails or essays. However, even with widespread adoption of those chatbots, corporations still face challenges occasionally. For example, these chatbots can produce inconsistent results because they depend on large stores of knowledge that will not be relevant to the query at hand.
Fortunately, Retrieval-Augmented Generation (RAG) has emerged as a promising solution to base large language models (LLMs) on probably the most accurate and up-to-date information. As an AI framework, RAG works to enhance the standard of LLM-generated Answers by basing the model on knowledge sources to enhance the LLM's internal information representation. IBM introduced its recent AI and data platform watsonx™, which offers RAG, back in May 2023.
Simply put, using RAG is like letting the model take an open-book exam, as you’re asking the chatbot to reply to a matter with all the data available. But how does RAG work on the infrastructure level? Using a mixture of Platform-as-a-Service (PaaS) services, RAG can run successfully and seamlessly, enabling generative AI outcomes for organizations across all industries using LLMs.
Why PaaS services are critical for RAG
Enterprise-grade AI, including generative AI, requires a highly sustainable, compute- and data-intensive distributed infrastructure. While AI is the important thing component of the RAG framework, other “ingredients” corresponding to PaaS solutions are a vital a part of the combination. These offerings, especially serverless and storage offerings, work fastidiously within the background and enable easier processing and storage of knowledge, resulting in increasingly accurate results from chatbots.
Serverless technology supports compute-intensive workloads like those provided by RAG by managing and securing the infrastructure around them, freeing up developers' time so that they can deal with coding. Serverless technology enables developers to construct and run application code without deploying or managing servers or backend infrastructure.
When a developer uploads data to an LLM or chatbot but isn't sure the best way to preprocess the info so it's in the correct format or filtered for specific data points, IBM Cloud® Code Engine can do all of that for them – simplifying the general strategy of getting correct results from AI models. As a totally managed serverless platform, IBM Cloud Code Engine can easily scale the applying using automation capabilities that manage and secure the underlying infrastructure.
Additionally, when a developer uploads the sources for LLMs, it can be crucial to have highly secure, resilient, and sturdy storage. This is very critical in highly regulated industries corresponding to financial services, healthcare, and telecommunications.
IBM Cloud Object Storage, for instance, provides security and data durability for storing large amounts of knowledge. With immutable data retention and audit control capabilities, IBM Cloud Object Storage supports RAG by helping to guard your data from tampering through ransomware attacks and ensuring it meets compliance and business requirements.
IBM's extensive technology stack, including IBM Code Engine and Cloud Object Storage, enables corporations across all industries to seamlessly access RAG and deal with using AI more effectively for his or her business.
The power of cloud and AI in practice
We have established that RAG is incredibly beneficial for enabling generative AI outcomes, but what does this appear to be in practice?
Blendow Group, a number one legal services provider in Sweden, handles a wide selection of legal documents – analyzing, summarizing and evaluating them, from court rulings to statutes and case law. As the team was relatively small, Blendow Group needed a scalable solution to support their legal evaluation. In collaboration with IBM Client Engineering and NEXER, Blendow Group developed an progressive AI-driven tool that leverages the excellent capabilities to enhance research and evaluation and streamline the legal content creation process, while maintaining the utmost confidentiality of sensitive data.
Using IBM's technology stack, including IBM Cloud Object Storage and IBM Code Engine, the AI ​​solution was tailored to extend the efficiency and breadth of Blendow's legal document evaluation.
The Mawson's Huts Foundation can also be a terrific example of how RAG will be used to drive higher AI outcomes. The foundation is devoted to preserving Mawson's legacy, which incorporates Australia's 42 percent territorial claim to Antarctica, and educating schoolchildren and others about Antarctica itself and the importance of preserving its pristine environment.
With The Antarctic Explorer, an AI-powered learning platform running on IBM Cloud, Mawson enables children and others to access Antarctica through a browser, regardless of where they’re. Users can ask questions through a browser-based interface and the training platform uses AI-powered natural language processing capabilities from IBM Watsonx Assistant™ to interpret the questions and supply appropriate answers with associated media – videos, images and documents – stored in and accessed from IBM Cloud Object Storage.
By leveraging infrastructure-as-a-service offerings along with Watsonx, each the Mawson Huts Foundation and Blendow Group can gain deeper insights from their AI models by simplifying the strategy of managing and storing the info they contain.
Enabling generative AI results with the cloud
Generative AI and LLMs have already proven that they’ve great potential to rework organizations across all industries. Whether it’s educating the overall population or analyzing legal documents, PaaS solutions within the cloud are critical to the success of RAG and the operation of AI models.
At IBM, we imagine that AI workloads are prone to form the backbone of mission-critical workloads and can ultimately house and manage probably the most trusted data. Therefore, the infrastructure that surrounds them have to be trusted and resilient by design. With IBM Cloud, organizations across all industries using AI can achieve higher levels of resilience, performance, security, compliance and total cost of ownership. Learn more about IBM Cloud Code Engine and IBM Cloud Object Storage below.
IBM Cloud Code Engine IBM Cloud Object Storage
