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Executive Summary

This report highlights the potential of large language models (LLMs), first brought to the mainstream by OpenAI’s ChatGPT. It details their inner workings and outlines how they can be integrated as agents within current technologies, data structures and industrial procedures, to create substantial market impact. We explore the Transformer architecture, its significance, and its limitations. Drawing upon meticulous analysis of recent academic research, commercial product launches, and contributions from the open-source AI community, this report guides readers through comprehensive explanations and intuitive graphical depictions of LLMs functioning as chatbots, reasoning companions, information retrieval systems and agent-style task completion aides. Our findings illuminate a future in which data management, machine learning and LLMs intersect to fuel unparalleled efficiency and innovation in the heavy industries sector.

Table of contents

Advances in data management and machine learning techniques have become rocket fuel for AI development
Transformer-based LLMs dominate today’s generative AI landscape
Ten applications of LLMs for industrial operations

Table of figures

Figure 1. The transformer architecture enables deep language understanding
Figure 2. Encoders distil the meaning of sentences
Figure 3. Transformer decoder LLMs generate text
Figure 4. LLMs can model and predict real-world cause and effect
Figure 5. Retrieval-augmented generation (RAG) can eliminate LLM hallucination
Figure 6. LLM agents can use tools to complete tasks

About the authors

Joe Lamming

Senior Analyst
Joe is a Senior Analyst in the Verdantix Operational Excellence practice. His current research agenda covers industrial DataOps, AI/ML analytics and applications of generative AI for industry and enterprise. Prior to joining Verdantix, Joe worked in the consumer electronics industry, where he gained experience in overseas manufacturing, product design and data science. Joe holds an MEng in Mechanical Engineering and Sustainable Energy Systems from the University of Southampton.

Malavika Tohani

Research Director, Operational Excellence
Malavika leads the Verdantix Operational Excellence practice. Her current research agenda focuses on digital technologies for Operational Excellence including digital twins and software solutions for industrial risk and asset management. Malavika has over 15 years’ experience in research and strategy consulting. Malavika previously worked at Frost & Sullivan, managing and delivering advisory projects for clients involving expansion, acquisition, benchmarking and product development strategies. Malavika holds a MSc in Economics from Madras School of Economics.

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