NLP (7): Prompt Engineering and In-Context Learning

Fri, 31 Oct 2025 09:00:00 +0000

The same model can produce a sharp answer or a confident hallucination. The difference lies in the framing, not the weights. A vague request like “analyze this text” yields a generic summary; a prompt with a role, two clear examples, and a strict output schema produces something a parser can use. Prompt engineering turns that gap into a repeatable system, not just a lucky shot.

In-Context Learning (ICL) is the mechanism that makes this work. When you include a few examples in the prompt, the model doesn’t retrain; it conditions its forward pass on those examples and effectively infers a task from them. Understanding ICL’s capabilities and limitations separates a developer who struggles with the model from one who guides it.

In-Context Learning on Chen Kai Blog

NLP (7): Prompt Engineering and In-Context Learning