The text discusses the challenges of choosing an appropriate embedding model for a search or RAG application, particularly when dealing with domain-specific data such as financial text. The authors highlight the need to consider not only general-purpose models like OpenAI's but also specialized models trained on specific fields like finance, healthcare, or legal text. They present a straightforward way to evaluate different embedding models using pgai Vectorizer, an open-source tool for embedding creation and sync, and demonstrate its use by comparing a general-purpose model against a finance-specialized model on real financial statements. The evaluation reveals significant differences in the ability of the two models to handle financial text, with the specialized model achieving higher accuracy, particularly in direct financial queries. The authors also discuss the trade-offs between cost, processing time, and accuracy, suggesting that domain-specific training can substantially improve the handling of financial terminology and concepts. They provide a framework for making decisions about choosing between general and finance-specialized embedding models based on practical factors such as document volume, search patterns, accuracy requirements, and cost constraints.