Large Language Models (LLMs) like ChatGPT have taken over the world like few other technologies – and are not just a brief tech hype, but a paradigm shift in how we interact with data, knowledge and decision making. The potential of this new technology is evident across all business sectors:
Say goodbye to information overload and welcome actionable insights. Personal voice assistants can analyze vast amounts of text, documents, and unstructured data in a fraction of a second and find the information you’re looking for from public and private sources, and customize it to suit your needs.
University lectures have long ceased to be purely lecture hall events, but with the help of language models, teaching is becoming more interactive and individualized. Directly integrated into the Moodle platform, students can now write with a chat bot that answers questions about the respective learning content, citing sources, summarizes chapters or creates test questions for exam preparation.
The Otto Group operates numerous online shops whose product ranges and catalogues differ greatly in some cases. However, if the product description is cleverly coded with the tools from the LLM area, the products of the respective shops can be translated into each other. In this way, customer-specific product recommendations can be generated across all shops.
The amount of information available in text form is enormous. In the case of an energy provider, this includes plant and error logs, reports on market and price developments, environmental regulations, CO2 data, and customer profiles and contracts. By using large language models, this information can be integrated and used in almost all steps of the value chain.
Have you already identified a concrete use case for your company that you would like to implement with us? Or is it still a vague idea that we want to substantiate together?
No matter where you are at the moment, it is not difficult to get started with AI in your company. We will be happy to take you on a joint journey to unfold the individual advantages of AI.
How much data is needed to use large language models effectively?
While traditional NLP models only used to work well when trained on their own data, today large language models can also handle unseen data. Accordingly, large amounts of data are not needed for model training, but only exactly the data you want to work with. This can be many thousands or just a handful.
Does data need to be labeled?
Since large language models do not necessarily require further training, labeled training data is not necessary. Nevertheless, the quality of the responses needs to be evaluated to identify areas for improvement. Accordingly, domain experts should collaborate with our team during development to enable a qualitative evaluation.
Is there a risk that ChatGPT learns from our data when I use it?
How is using an LLM with our company data different using the OpenAI Playground?
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