Fine-tuning is a term used in the fields of artificial intelligence and digital transformation. It describes an important step in the training of AI models such as voice assistants or image recognition programmes.
Fine-tuning involves taking an already pre-trained AI model and adapting it to a company's specific requirements or data. This can mean, for example, that a language model for general texts is trained again with texts from your own company. This enables the AI to respond better to industry-specific questions or understand company-specific terms.
An illustrative example: A customer service company wants to use a chatbot that responds particularly well to the language of its own customers. To do this, it uses an AI model that has already mastered the basics of language comprehension. Through fine-tuning with its own customer dialogues, the chatbot learns to recognise typical questions, problems and the style of the customer and to provide suitable answers.
Fine-tuning makes artificial intelligence significantly more effective in practical use and helps companies to design their digital tools in a customised and customer-oriented way.
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