Company earnings calls are crucial events that provide transparency into a company’s financial health and prospects. Large language models (LLMs) offer a promising approach to automatically generate the first draft of earnings call scripts from financial data and past examples. We evaluate two methods: 1) Few-shot prompt engineering with a state-of-the-art model, and 2) Fine-tuning a language model on earnings call transcript data. Our results indicate both approaches can produce coherent scripts covering key metrics, updates, and guidance. However, there are trade-offs in comprehensiveness, hallucinations, writing style, ease of use, and cost. We discuss the pros and cons of each method to guide practitioners on effectively leveraging large language models for this financial communication task.