Series of articles on Large Language Models
ChatGPT in the industry
ChatGPT was the hype topic last year. This technology is expected to enter the industry in 2024. A new series of articles explores the opportunities and challenges of ChatGPT for the industry.
Part 1: ChatGPT in the industry
One technological innovation that really stood out last year was the release and widespread accessibility of a type of artificial intelligence called ChatGPT, published by the company OpenAI. And although it had been apparent for some time in the scientific field that a major breakthrough that could be described as revolutionary was imminent, it was surprising and overwhelming for the rest of the world to have a "thing" available that could be described as intelligent in the true sense of the word.
Part 2: Prompt engineering
Part 2 of the article series focuses on a specific aspect: How are good requests to ChatGPT structured or technically formulated? How does the systematic formulation of effective prompts work? This skill, called prompt engineering, is at the heart of the efficient use of LLMs in enterprise applications and is an important key to unlocking the full potential of ChatGPT.
To introduce the topic of prompt engineering, this article focuses on direct interaction with an artificial intelligence such as ChatGPT via a web interface. Figuratively speaking, this user level corresponds to the tip of the iceberg when dealing with LLMs. In addition to the prompt explicitly formulated by the user, there are many other influencing variables that determine the quality of the result.
Part 3: The intelligent assistant
This part of the article series "ChatGPT in industry" focuses on how the technology of Large Language Models (LLM) is finding its way into industrial products and services in the form of intelligent assistants.
How can user interfaces for complex industrial products and services be kept as simple and comprehensible as possible? In this context, wouldn't it be tempting to have an intelligent assistant at hand at all times? This would understand questions and instructions regardless of language and then provide the precise information required to rectify a fault or for the next optimal operating step.
Part 4: Catalyst of an agile development process
Part 4 of the article series deals explicitly with the interplay between AI and agile working methods in product development.
The main claim of agile methods is to emphasize iterative processes, continuous improvement and the early incorporation of customer feedback. In contrast to traditional, waterfall-like approaches, in which requirements are defined comprehensively at the beginning and then implemented step by step, agile methods welcome the gradual increase in knowledge on the customer side and in the team. In contrast to the idea of being able to firmly define the requirements at the beginning of a project in waterfall models, this requires a continuous stream of changes. If implemented correctly, this ultimately leads to a product that is better adapted to requirements in a shorter project lead time. Agile methods also aim to systematically identify "work that does not need to be done" in order to achieve high development performance. This means that tasks with little or no added value for the product are not simply accepted with a shrug, but actively avoided.
More articles in the "ChatGPT in the industry" series
Part 5: Requirements management
Can generative AI be inventive? In view of the rapid evolution of AI, this can perhaps be answered with a "yes" at present. In any case, LLMs are already useful tools when it comes to formulating requirement descriptions.
The last article in this series (issue 5, pages 14-17) focused on the possible support for the description and formulation of work packages using generative tools such as ChatGPT. The legitimate question that arises here is: Where do the requirements that are described in the work packages for technical implementation come from? How can generative methods be used in requirements analysis and description in the run-up to the project and then during the project?
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