The value of Large Language Models for enterprise Part.4

AIGenerative AI2023-04-04

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Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.4, the final installment, is a continuation of “How I can use Large Language models in my business?”, with “6. Enabling custom AI systems” and “Conclusion”

Read Part.1

Read Part.2

Read Part.3

How I can use Large Language models in my business?

Part.4 is a continuation of Part.2 and Part.3, we have put together a summary of the various applications of LLMs in the enterprise context. This overview will give you a sense of the types of problems that LLMs can help you solve, and the ways in which they can be integrated into your existing workflows.

6. Enabling custom AI systems

Large language models can help make the development of customized AI models more efficient and cost-effective by enabling the extraction of insights from unstructured data and feeding it to scientific-based systems. This can lead to optimized industrial processes, improved scientific simulations, better demand forecasting, and more. By automating some of the data extraction and processing tasks, using generative AI can also help reduce costs associated with manual labor and increase productivity.

When it comes to scientific simulations, large language models can be used to extract and crawl data from public and private data repositories that can be fed into simulators to improve accuracy and reduce the computational cost of simulations. This can lead to more accurate predictions and better understanding of complex physical phenomena.

By analyzing electronic health records and other patient data, a model could be developed to create personalized recommendations for treatment plans, medication dosages, and more. These recommendations can be tailored to specific healthcare concerns, such as chronic diseases or mental health disorders, and can help improve patient outcomes while reducing healthcare costs.

In infrastructure, these models can be used to extract insights from various data sources such as traffic patterns, energy consumption, and environmental impact. This data can then be fed into a model that optimizes the cost and benefit of developing a certain piece of infrastructure, such as a new highway or a wind farm.


In conclusion, large language models have the potential to revolutionize the way businesses operate, from knowledge management and business intelligence to customer service and vertical-specific AI models. By leveraging the power of language, businesses can extract insights from unstructured data and make informed decisions that drive growth and efficiency. With their ability to understand complex patterns and dependencies in language, large language models are a powerful tool for unlocking the potential of big data and advancing AI capabilities. As these models continue to evolve and improve, we can expect to see their impact on businesses grow even more significant in the coming years.



Headshot of Tiago Ramalho

Co-founder and CEO

Tiago Ramalho

Tiago holds a Master's degree in Theoretical/Mathematical Physics and a PhD in Biophysics from Ludwig-Maximilians University Munich. After graduation, he joined Google DeepMind as a research engineer. There he worked on a number cutting-edge research projects which led to publications in international machine learning conferences and scientific journals such as Nature. He then joined Cogent Labs, a multinational Tokyo based AI start-up, as a lead research scientist. In August 2020 co-founded Recursive Inc, and is currently CEO.