Welcome back to the second part of our series about identifying use cases for Large Language Models (LLMs) in the enterprise. In part 1, we delved into five use cases that fit into what I’ve termed as ‘classical NLP problems.’ Today, we’ll explore the remaining three use cases, representing unique opportunities for applying the emerging reasoning power of LLMs in enterprise environments.
The first is what I call ‘High-Quality Document Generation and Validation.’ This term captures situations where your business invests significant time generating documentation that needs to be precisely written. Usually, this relates to legal, regulation, compliance, or law-enforcement situations. New LLMs excel in this use case. They can assist relatively new employees in generating higher quality documentation consistent with the business’s style and domain-specific needs. Moreover, these models can perform validation on the documentation, surfacing potential errors or omissions, and relieving managers of the most tedious part of their tasks.
The second use case is ‘Unified Natural Language Query.’ This one resonates with anyone who’s ever struggled to get straightforward answers from their data, often because they don’t know or don’t want to learn complex languages like SQL, or because the data in question is dispersed. Unified Natural Language Query enables anyone to ask simple questions about their data, even if the data is spread across multiple formats and sources. It’s going to be a killer app for LLMs in the enterprise because there’s so much pent-up demand for this capability.
Our third and final use case is ‘Routine Task Automation.’ This is leveraging the reasoning power and task orchestration capabilities of LLMs by deploying them in control frameworks like LangChain or Hugging Face’s Transformer Agents. It involves automating routine, repeated tasks currently performed by people, tasks that involve gathering and analyzing bits of information from various sources, reasoning across them, and making quick decisions.
So, these are the top three emerging use cases where LLMs, such as GPT-4, offer unique capabilities that were simply not possible with previous generations of language model technology, like the BERT family of NLP transformers.
My recommendation would be to start with Unified Natural Language Query and High-Quality Document Generation and Validation. Not only will these immediately spark interest among your business users, but they also lay the groundwork for creating transformative capabilities. This approach will help your organization truly grasp the power of this technology. I hope you find this guidance helpful. Have a wonderful day.
Prolego is an elite consulting team of AI engineers, strategists, and creative professionals guiding the world’s largest companies through the AI transformation. Founded in 2017 by technology veterans Kevin Dewalt and Russ Rands, Prolego has helped dozens of Fortune 1000 companies develop AI strategies, transform their workforce, and build state-of-the-art AI solutions.