Advanced prompting in action – few-shot learning and prompt chaining

In few-shot settings, the LLM is presented with a small number of examples of a task within the input prompt, guiding the model to generate responses that align with these examples. As discussed in the prior chapter, this method significantly reduces the need for fine-tuning on large, task-specific datasets. Instead, it leverages the model’s pre-existing knowledge and ability to infer context from the examples provided. In Chapter 5, we saw how this approach was particularly useful for StyleSprint by enabling the model to answer specific questions after being provided with just a few examples, enhancing consistency and creativity in brand messaging.

This method typically involves using between 10 and 100 examples, depending on the model’s context window. Recall that the context window is the limit of tokens a language model can process in one turn. The primary benefit of the few-shot approach...