The power of words: How big language models are driving smart self-learning products

07/31/2023 | Blog Blog

Language models are a long-standing component of natural language processing (NLP). In recent years, however, a new type of language model has emerged: large language models (LLMs). In contrast to traditional approaches, large language models are based on advanced AI models based on neural networks. They are characterized, among other things, by their ability to generate human-like texts and thus increase the productivity of companies.

ChatGPT is a specialized implementation of a large language model designed to generate human-like text in a conversational situation.

These large language models are trained through extensive pre-training with large all-encompassing datasets to recognize complex patterns and structures. This teaches them to accurately predict the next word or phrase in a sentence. Since this pre-training also combines training data such as code repositories, technical forums, programming platforms and product documentation, large language models provide a strong foundation for the development of intelligent self-learning products. They also serve to promote cross-industry profitability and competitiveness.

The use of these large language models opens up a wide range of possibilities, such as the creation of advanced chatbots with natural dialog, automated translations and context-specific text generation. Furthermore, intelligent products and applications can be developed which, in addition to understanding and generating natural language, also perform scenarios for dialog-like, continuous code generation and conversion.

This results in intelligent products that continuously explore their environment through autonomous learning, develop ever more sophisticated capabilities and constantly make new discoveries without human intervention. This creates innovations in technology-driven sectors, from industry to healthcare and mobility.

Choosing the right prompt plays an important role when using large language models and influences the quality of the results.

Large language models are able to solve tasks using prompting techniques without additional model training. The task to be solved is presented to the model in written form as part of a textual prompt. Selecting the "right prompts" is of great importance to ensure that the model delivers high-quality and accurate results for the tasks set. By cleverly formulating the text prompts, the performance of the model can be optimized by steering it in the right direction and generating appropriate solutions.

Prompt engineering is therefore the ability to create effective prompts to obtain the desired outputs from the base models of generative AI models. This is an iterative process in which targeted prompts are developed to achieve defined goals. The ability to select appropriate prompts therefore plays a central role in the use of large language models and has a major impact on the quality and accuracy of the results.

Large language models offer enormous potential, as they are now also used in the context of company data and can therefore support your projects and tasks in innovative ways. They enable you to speed up time-consuming tasks and solve complex problems through automated dialogs.

We support you in selecting a suitable model that meets your requirements and work with you to develop an effective, customized prompt strategy for your company data.

About the author

Rainer Duda is a Data & AI Consultant at M&M Software and advises companies on the development of data-driven business models and the realization of AI-supported applications. He worked for many years as a data scientist at the renowned Institute for Telematics (TECO) at the Karlsruhe Institute of Technology (KIT), including on the Smart Data Solution Center Baden-Württemberg (SDSC BW) project, and holds lectureships in multivariate statistics and applied data science.

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