Fine-tuning versus in-context learning

We learned how in-context learning could allow StyleSprint’s model to handle a diverse range of customer queries without requiring extensive retraining. Specifically, a few-shot approach combined with RAG could facilitate quick adaptation to new inquiries, as the model can generate responses based on a few examples. However, the effectiveness of in-context learning heavily relies on the quality and relevance of the examples provided in the prompts. Its success would also rely on the implementation of RAG. Moreover, without fine-tuning, responses may lack consistency or may not adhere as strictly to StyleSprint’s brand tone and customer service policies. Finally, depending entirely on a generative model without fine-tuning may inadvertently introduce bias, as discussed in Chapter 4.

In practice, we have two very comparable and viable approaches. However, to make an informed decision, we should first perform a more in-depth comparis...