Large Language Models (LLMs) like ChatGPT can perform tasks they were not specifically trained for through zero-shot and few-shot prompting techniques. Zero-shot prompting involves asking the model to complete a task without prior examples, relying on its general understanding of language and pre-existing knowledge. This method is effective for simple or exploratory tasks but struggles with complex ones requiring specific outputs. Few-shot prompting, on the other hand, involves providing a few examples to guide the model, allowing it to adapt to specific tasks or formats without altering its parameters permanently. While this approach enhances task accuracy and is suitable for situations with limited training data, it can be inefficient for general knowledge tasks and may not handle complex reasoning well. Understanding the strengths and limitations of these techniques allows users to better harness the capabilities of LLMs in various applications.