The article explores the contrasting methods of fine-tuning and retrieval-augmented generation (RAG) in the context of customizing large language models (LLMs). Fine-tuning involves adjusting a base model with a custom dataset to change its behavior, while RAG utilizes few-shot learning and context-injection to achieve similar outcomes. The text argues that fine-tuning often results in questionable performance gains, complexity, slower iteration, loss of model generality, and higher costs compared to RAG, which is more efficient and adaptable at inference time. Despite fine-tuning's advantages in achieving specific output formats and complex reasoning, the article suggests that RAG, with its simplicity and lower costs, is often a more practical choice for startups. It concludes by recommending the use of prompt management systems like PromptLayer to further streamline the development process and enhance model performance without resorting to fine-tuning.