Predicting demand for new products is an important and challenging problem in marketing, especially for product categories where brand is a key driver of choice. In these settings, observed product attributes do not explain choice patterns well, which makes predicting sales of a new product as a function of marketing mix variables intractable. To address this problem, I develop a scalable framework that enriches structural demand models with large language models (LLMs) to predict consumer preferences for new brands. After estimating preferences for existing brands using a structural model, I use an LLM to learn a mapping from text descriptions of the brand and consumer to these estimated preferences. When fine-tuned in this way, I show that the tuned LLM is able to generalize to previously unseen brands that were excluded from the training sample. In contrast, conventional models based on text embeddings return predictions with zero or even negative correlation with the actual utilities. My fine-tuned LLM achieves the first informative predictions of consumer preferences of new brands from raw text, with a 25% smaller mean squared error and correlation between predictions and held out preferences of 0.34. Additionally, I combine causal estimates of the price effect from instrumental variables methods with the LLM predictions to enable pricing-related counterfactuals. More broadly, this approach illustrates how new kinds of questions can be answered by using the capabilities of modern LLMs to systematically combine the richness of qualitative data with the precision of quantitative data.