Prompt engineering is the practice of crafting precise and effective input prompts to elicit desired responses from large language models (LLMs). This method focuses on designing the exact wording, structure, and context of the prompt to guide the model towards generating specific outputs. It requires an understanding of the model’s capabilities and the nuances of language to maximize the quality and relevance of the responses.

Unlike [flow engineering](/glossary/flow-engineering), which involves a multi-step, iterative process to refine outputs, prompt engineering aims to achieve the desired result with a single, well-constructed input. This approach is particularly useful for straightforward tasks where the model's initial response is expected to be accurate and sufficient. However, it can be limited in handling complex problems that require deeper analysis and iterative refinement.

Prompt engineering is essential in scenarios where quick, efficient responses are needed, and the task complexity is manageable with a single input. It is a critical skill for developers and users who interact with LLMs, enabling them to harness the model's full potential by providing clear and concise prompts that lead to high-quality outputs.