Prompt Engineering refers to the process of designing and optimizing prompts to effectively communicate with an AI model. It involves crafting inputs in a way that guides the AI to produce the desired output. This process is crucial in leveraging the full potential of AI models, as the quality and relevance of the output largely depend on the prompt's structure, context, and clarity.
Common techniques include clear task instructions, explicit constraints, representative few-shot examples, role definition, output priming, and a precise output contract. Prompt changes should be tested through evaluation rather than judged from a small number of examples.
For applications that combine instructions with retrieval, tools, memory, and conversation state, prompt engineering is one part of the broader discipline of context engineering. Chat-style prompts are assembled from roles such as the user message and assistant message.
Meta-prompting uses a model to generate, critique, or optimize prompts, but generated prompts still need evaluation against real tasks.
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