Zero-Shot vs. Few-Shot Prompting
Zero-shot prompting asks the model to do a task with instructions alone. Few-shot prompting adds a handful of worked examples before the real question, showing the model exactly what a good answer looks like, not through training, but through the context-window mechanism from lesson 1.
Predict before you look
Both prompts ask for the same JSON classification. Before you look, do you expect the two outputs to differ in which category they pick, in their format, both, or neither?
Both got the category right. The real difference is format. Zero-shot wrapped its answer in a ```json code fence, reasonable enough for a chat interface, but it would break a naive JSON.parse() call in real code. Few-shot's two examples never used a code fence, so the model's output didn't either, clean, parseable JSON on the first try.
That's the more common story with few-shot prompting. On easy tasks the content of the answer often doesn't change, because a capable model already gets it right. What reliably changes is format adherence, exact labels, exact structure, exact tone, because examples pin down conventions that instructions alone tend to leave the model guessing about.
In the transcript above, what did few-shot prompting actually fix?