Towards Next Gen AI: What You Need to Know about Large Language Models

Gen AI

Generative AI (Gen AI) may be all the hype at the moment, but the foundational technology has a long history dating back to the 1960s. From predictive models utilizing machine learning to deep learning and neural networks, such as convolutional neural networks, and prescriptive models that enable precision modeling, advanced AI has been a gradual evolution punctuated with bursts of rapid advances.   

And now, gen AI—built with transformer models—is set to change the world as we know it. While a combination of machine and deep learning is still in play, gen AI is different than past AI technologies, because it’s trained to generate content that’s contextual and, in many ways, more meaningful. To truly understand gen AI’s potential—and its constraints—you must first understand the basic aspects of how it works, which starts and ends with large language models (LLMs).   

What Is a Large Language Model?

LLMs are AI technologies that can handle massive amounts of data. They can process the data, place it within the appropriate context, personalize it according to specific needs or preferences, and provide answers to questions or solutions to problems using natural language—the kind of language that you and I write and speak in. These AI models excel in understanding and generating human-like text, allowing for more sophisticated and natural interactions with users. Now when it comes to gen AI, the definition has expanded beyond a simple model. It now encompasses a range of approaches, including language models, image generators, and generative adversarial networks (GANs).   

Gen AI is distinct from past AI models because it’s trained on a significantly larger dataset. And it can handle billions of parameters, which are like adjustable settings on a synthesizer, controlling various aspects of the output—such as volume, tone, and oscillation. To give you an idea, ChatGPT-3, a specific generative AI model, was constructed using a whopping 175 billion parameters (that’s a lot of adjustable settings!). Due to its extensive size, generative AI becomes exceptionally powerful in doing all the things we marvel at today, everything from writing poems to creating images from text.  

To gauge potential use cases for your business, it’s important to know that gen AI is a technology, not a solution. What you do with it becomes the business solution, and how you adjust the parameters to align with your use cases becomes essential. 

Common Business Use Cases for Gen AI

Some of the ways to use gen AI will already be familiar to the 100 million or so ChatGPT users. The important takeaway from these use cases is not necessarily how to monetize gen AI, but rather that it’s an emerging technology that has specific and immediate applications that are going to expand as it evolves. Here are the broadest use cases that gen AI is currently being used for:  

  • Text generation: One of the primary use cases of gen AI is the automated generation of text. These text generation models can assist in crafting personalized messages, generating web, social, or email copy, and creating written content tailored to specific needs. In the context of contact centers, for example, it can be used to enhance customer interactions and provide advisors with quick, real-time assistance in responding to customers.  
  • Image generation: While image generation may not be a prevalent use case in enterprise applications, it holds potential in fields like design, marketing, and gaming. Gen AI can be used to turn word prompts into images, aiding in training purposes, architectural visualizations, and even the creation of distributed system blueprints.  
  • Video synthesis: In an era dominated by visual experiences, gen AI can accelerate video production. This application is particularly useful for augmented reality (AR) and advertising, where personalized videos can be created with the assistance of AI, eliminating the need for extensive manual editing. Video training for contact center advisors, for example, becomes much more accessible and easier to produce.  
  • Knowledge management: The ability to categorize and sort large volumes of data enables the identification of common themes and trends, supporting informed decision-making and more targeted strategies. For advisors, getting access to the right information to support a customer can be instrumental in effective help desks and gen AI can make that much easier.  
  • Language Translation: Advisors can potentially receive calls and chats from around the world in any language and have them translated in real-time by gen AI. As more LLMs are trained in other languages, quality and availability will continue to improve.  
  • Summarization and Paraphrasing: Entire customer calls or meetings could be efficiently summarized so that others can more easily digest the content. Gen AI can take large amounts of text and boil it down to the most essential information, even providing feedback to advisors based on their performance.  
  • Developer Experience: Gen AI’s potential to reimagine the developer experience and improve data integration is often overlooked. By acting as an AI assistant, it can guide developers in coding tasks and provide structured information for efficient problem-solving. 

Towards a New Model

While gen AI has a lot of promise, it’s important to understand that gen AI is not a ready-made solution and is instead a versatile technology. Organizations must first address whether gen AI is a good fit for your needs and also ensure data privacy and compliance needs are met. Every business will have to explore specific use cases to address their unique challenges. And for most companies, they’ll need to do this without the benefit of their own LLM.  

LLMs demand substantial investments and ongoing operational costs. For instance, ChatGPT costs up to $700,000 each day to run. Building an LLM transcends mere data collection. It requires creating the perfect ecosystem, having the right people with deep knowledge of AI, and defining a clear business model. Monetization strategies and target audiences must be carefully considered.  If the model is tailored for internal use, it becomes an asset. However, venturing into the realm of established giants like OpenAI’s ChatGPT, NVIDIA’s NeMo, or Google’s Bard and LaMDA demands a competitive edge that only a handful of companies can achieve.  

But you can tap into the power of these preexisting LLMs through open APIs. By connecting to LLMs through APIs, businesses gain access to an incredible realm of possibilities. When you incorporate your proprietary data, fostering localized knowledge bases, you can leverage LLMs to maximize operational efficiency. Imagine creating advisor assist and knowledge base models within your contact center environment, helping to eliminate repetitive tasks, while providing advisors with individually tailored guidance and feedback, enabling them to focus on solving more complex customer issues.    

What You Can Do Today

At Concentrix, we offer a range of solutions leveraging gen AI technology and techniques. Our enterprise models cater to both industry-specific and cross-functional needs, ensuring versatility and adaptability. We also provide seamless integration with APIs, empowering businesses to develop custom solutions. Our collaboration with partners like OpenAI, Microsoft, Google, Salesforce, and others ensures compliance and data security, as we enhance the capabilities of these models through localized knowledge bases.  

Not every enterprise needs to aspire to the colossal scale of industry giants. Concentrix understands that success lies in making the most of your own proprietary data. Let us help you roadmap your potential use cases for gen AI.  

Raja-Roy.png

Raja Roy

EVP of Digital Engineering & Cloud Engineering