Don't Fight the Weights

Today, in-context learning is a standard trick in any context engineer’s toolkit. Provide a few examples illustrating what you want back, given an input, and trickier tasks tend to get more reliable. They’re especially helpful when we need to induce a specific format or style or convey a pattern that’s difficult to explain1.

When you’re not providing examples, you’re relying on the model’s inherent knowledge base and weights to accomplish your task. We sometimes call this “zero-shot prompting” (as opposed to few shot2) or “instruction-only prompting”.