Large language models can generate impressive results, but they don’t learn or evolve after training. In-context learning offers a powerful workaround—letting us guide a model’s behavior using carefully designed prompts and examples, without the need for retraining. This post breaks down the core techniques behind in-context learning, from zero-shot to few-shot prompting, and shows how to get more consistent, structured outputs from LLMs.