How to Solve Complex Tasks with a Generative AI-Enabled Hybrid Architecture

With the immense complexity of enterprises today, the amount of information available and variety of tools across the business are often siloed or even duplicated. Being able to find answers to complicated questions and solve complex tasks is often difficult, especially under time and resource constraints. This is where a systematic approach for AI-enabled decision making can help.

By now, you’ve probably heard of the generative AI product ChatGPT and, most likely, experimented with it. In the last few months, the wonders and dangers of this new tool have been discussed by governments and lawmakers, educators and students, tech and business crowds, teenagers and their grandparents. The latest advances in large language models (LLMs), like ChatGPT, have demonstrated the possibilities of what the future of generative AI could look like for businesses that are willing to embrace innovation. It is also clear that the rate of innovation in this space is not only greater than in previous years, but it is also more disruptive.

Clearly, LLMs have demonstrated their effectiveness in solving numerous natural language problems. However, it is important to acknowledge their limitations when it comes to handling complex, multi-step tasks, which can arise from the multi-nodal nature of these tasks or the associated costs involved. Solving multi-step tasks often requires a complex ecosystem of AI models and their enabling technologies and services to drive real incremental value for businesses. An anecdotal example in this paper — how to accurately count zebras across three photos – illustrates this complexity. Counting zebras is a straightforward task for us humans, but when it comes to AI, things become much more complex. Counting zebras then requires the use of image captioning, object detection, text classification, natural language processing and computer vision to accurately complete this task.

LLMs can be the orchestrators of that complexity and that’s where we’d like to focus — on a great example that can be found in the same paper we referenced earlier, which outlines how an existing wide range of models hosted on Hugging Face Hub can be employed, with the help and guidance of LLMs, to tackle complex tasks. This approach leverages the analytical and communication powers of LLMs and the wealth of pre-trained task-specific machine learning (ML) models to solve tasks across four stages — task planning, model selection, task execution and response generation.

To differentiate in the market today, businesses need to have the right ecosystem, enabling technologies and data assets in place to bring the power of AI to life. Below we’ll provide insight into what we believe are the most important components of that ecosystem and where you should begin your AI journey…

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Learn how we can help your business maximize the benefits of Generative AI here.