Beyond the Buzz: What Real-World GenAI Integration Will Look Like for Most Companies
Are you on the AI train yet? According to a 2024 study conducted by the McKinsey consulting firm, 72% of organizations have adapted some form of AI in at least one business function. While AI by itself has proved valuable, it is the promise of GenAI that has captured the attention of the world. Gartner predicts that by 2026, over 80% of enterprises will have used GenAI APIs or GenAI-enabled applications in production environments. In 2023, less than 5% of companies did so. No doubt, the GenAI train is leaving the station. Do you have your destination in mind?
What does GenAI Really Mean for Most Organizations?
Of course, just because a lot of companies will be using GenAI, doesn’t mean they will be doing it effectively. GenAI is powered by large language models (LLMs), which require immense investment, expertise, and computational resources to develop and maintain. For most organizations, building and operating such sophisticated infrastructures is far beyond their capabilities or budgets.
Nearly everyone instead will be relying on accessing third-party LLMs provided by major tech companies and platforms. Utilizing these third party LLMs, however, introduces challenges such as cost management, data privacy concerns, and integration with existing systems and workflows. In other words, the emphasis is not to reinvent the wheel, but to understand how to effectively customize and implement GenAI solutions for your specific organization.
The Weaknesses of LLMs by Themselves
While it is easy to marvel at these LLMs, they aren’t the panacea you might think. Have you ever had a conversation with someone who is all over the map when having a discussion? LLMs have some inherent weaknesses.
- While LLMs have broad general knowledge, they often lack specific domain expertise or up-to-date information for particular industries
- LLMs are trained on data up to a certain point in time, making it difficult to update their knowledge without extensive retraining or organizations
- Believe it or not, LLMs can sometimes produce inaccurate or false information, known as “hallucinations”
- The use of vast amounts of training data raises issues about data privacy and potential misuse of personal information
Integrating Your Own Knowledge Base
What if you could have the best of both worlds – the computational intelligence of an LLM with the vast information available on the internet, while also incorporating your organization’s proprietary knowledge base? This is the premise behind Retrieval-Augmented Generation (RAG). The RAG redirects a LLM to retrieve relevant information from your own authoritative, pre-determined knowledge sources, thus extending its knowledge base and giving you greater control of its information retrieval. With RAG, the model can remain current and tailored, providing answers that blend its broad computational understanding with the specifics of your organization’s context. With RAG you get better accuracy and relevance along with enhanced security thanks to the reduced exposure of sensitive organizational data to external services.
Uses of a RAG Enhanced LLM
Imagine the ways that you can supercharge the experience of your customers with a customized LLM. Customers can engage with RAG-powered chatbots to get accurate and assured up-to-date responses by accessing current product information and customer data. These chatbots can provide tailored engagement for customers of different cultural regions and dialects. The use cases of a RAG enhanced LLM extend beyond customer interaction, however. Here are just some of the potential use cases:
- Employees can have their own internal AI agent to provide accurate answers to employee questions about company policies, procedures, and workflows or guide new hires in their onboarding process
- Enable teams to seamlessly access up-to-date project files, technical documents, or meeting notes
- Query specific data points or insights from research papers, proprietary databases, or competitive analysis documents.
- Generate tailored reports by integrating company-specific data with broader industry knowledge
The use cases are potentially unlimited as organizations learn how to transform their knowledge assets into a competitive advantage.
The Nutanix Simplified GenAI Solution
While customizing and optimizing an LLM is a lot easier than creating one from scratch, there is still a great deal of complexity, required dependences and infrastructure you need. For starters, you need Kubernetes as it provides the dynamic scaling of compute resources for LLM retrieval services based on query load. You will also need scalable persistent volumes for storing models and data not to mention layers of underlying services and software. It can be confusing to know where to even begin.
It would be nice if you could get an appliance-like solution for this. The good news is that you can. Nutanix Enterprise AI gives customers access to a wider range of GenAI models and tools to simplify the top GenAI opportunities for the enterprise. This offering can be deployed on any Kubernetes platform, including edge, core data centers, and public cloud services. Working with a business partner like Evolving Solutions, a wide variety of use cases can be deployed and start adding value to your business without some of the risks associated with public LLMs.
It only makes sense that Nutanix has done this, because they have done it before. They revolutionized the industry with HCI, seamlessly integrating all the essential components of a networked server ecosystem into a single, scalable stack. Now they have done the same by streamlining the approach to implementing your own GenAI strategy as a hybrid multi cloud data platform.
How Evolving Solutions can Help
Nutanix has the out-of-the-box solution to streamline your GenAI project. Now you just need the guidance of someone who has expertise in implementing it. That is where Evolving Solutions comes into play. Our team of innovators and technologists has years of experience assisting clients with Kubernetes environments, data availability, automation, and AI-driven solutions. Instead of starting with technology-focused discussions, we prioritize understanding your business needs, objectives, and goals. By using this client-centric approach, Evolving Solutions can ensure that any implemented GenAI solution properly aligns with your business directives, providing rapid value and advancing your digital transformation journey.
