When reading a text, it is common to get stuck on unfamiliar words that are difficult to understand in the local context. In these cases, we use dictionaries or similar online resources to find the general meaning of the word. However, maintaining a handwritten dictionary is highly resource demanding as the language is constantly developing, and using generative language models for producing definitions could therefore be a more efficient option. To explore this possibility, this thesis performs an online survey to examine if GPT could be useful for defining words. It also investigates how well the Swedish language model GPT-SW3 (3.5 b) define words compared to the model text-davinci-003, and how prompts should be formatted when defining words with these models. The results indicate that text-davinci-003 generates high quality definitions, and according to students t-test, the definitions received significantly higher ratings from participants than definitions taken from Svensk ordbok (SO). Furthermore, the results showed that GPT-SW3 (3.5 b) received the lowest ratings, indicating that it takes more investment to keep up with the big models developed by OpenAI. Regarding prompt formatting, the most appropriate prompt format for defining words is highly dependent on the model, and the results showed that text- davinci-003 performed well using zero-shot, while GPT-SW3 (3.5 b) required a few shot setting. Considering both the high quality of the definitions generated by text-davinci-003, and the practical advantages with generating definitions automatically, GPT could be a useful method for defining words.