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Prompt engineering comprises principles and techniques for crafting inputs to large AI models to elicit desired answers, formats, and behaviors. It combines linguistic precision, iterative testing, and context design. Goals are reliability, robustness, and efficient use of models across technical and product-focused applications.
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Prompt engineering is the systematic design of instructions, context, and output requirements for AI models so tasks are handled usefully and consistently.
The term emerged with practical use of large language models and initially referred to crafting effective instructions. As models became more capable, it expanded to examples, data context, tools, output formats, and evaluation.
Describe the task, context, constraints, and desired result so another person could review the instruction. Add examples and a format where useful, test representative cases, and treat prompts as changeable software artifacts.
The instruction states the goal, work steps, and priorities for the desired model response.
Relevant information and examples reduce ambiguity and demonstrate the expected pattern.
Prompts are tested against cases; one good answer does not establish reliable quality.
Prompt engineering helps make AI tasks more reproducible, testable, and usable in workflows. It does not replace domain review or security, privacy, and authorization rules.
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