Fine-tuning is a term from the fields of artificial intelligence and digital transformation. It describes an important step in training AI models such as voice assistants or image recognition programs.
Fine-tuning involves taking a pre-trained AI model and specifically adapting it to the particular requirements or data of a company. This can mean, for example, that a language model for general texts is retrained with texts from the company itself. This allows the AI to respond better to industry-specific questions or understand company-owned terms.
A clear example: A customer service company wants to implement a chatbot that is particularly good at understanding its customers' language. To achieve this, it uses an AI model that already masters the basics of language comprehension. By fine-tuning with its own customer dialogues, the chatbot learns to recognise typical questions, problems, and the customers' style, and to provide appropriate answers.
Fine-tuning makes Artificial Intelligence significantly more effective in practical application and helps companies to shape their digital tools individually and with a customer focus.
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