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Prompt engineering structures natural-language prompts to elicit specified outputs from generative AI models.
Prompt engineering is the process of structuring natural-language inputs (prompts) so a generative AI model produces specified outputs. It involves designing and refining the wording and structure of instructions—often by adding relevant context, specifying the desired output style, and assigning roles—to guide the model toward more accurate, relevant, and consistent results. In practice, prompt engineering may use techniques such as few-shot prompting, chain-of-thought prompting, and role assignment, and it can be applied to both text-based and multimodal models (e.g., text-to-image). The purpose of prompt engineering is to improve model performance despite the model’s sensitivity to prompt details. Research shows that small changes in phrasing, formatting, or example ordering can significantly affect outcomes, so effective prompting requires understanding how the model interprets language. Related work includes context engineering, which manages non-prompt inputs such as system instructions, retrieved knowledge, tool definitions, and metadata to improve reliability and efficiency in production systems. Prompt engineering has also expanded into automated methods (e.g., retrieval-augmented generation) and optimization approaches that search for better prompts, while also raising security concerns such as prompt injection attacks.
Prompt engineering structures natural-language prompts to elicit specified outputs from generative AI models.
It improves accuracy and usefulness by accounting for how models interpret language and by using techniques like few-shot prompting and chain-of-thought prompting.
Because model performance is highly sensitive to prompt wording and structure, prompt engineering often requires careful refinement and evaluation.
Context engineering complements prompt engineering by managing additional context elements (system instructions, retrieved knowledge, tools, metadata) for reliability and efficiency.
The field includes automated prompt generation/optimization methods (e.g., RAG) and security risks such as prompt injection attacks.
The process of formulating and refining natural-language prompts to optimize the outputs of a generative AI model toward a desired outcome.
Natural-language text that describes and prescribes the task an AI should perform, often including context and output requirements.
A related discipline focused on managing non-prompt context supplied to a generative AI model, such as system instructions, metadata, tools, and retrieved knowledge.
A model capability to temporarily learn from examples provided in the prompt without changing the model’s parameters.
A prompting approach that includes a small number of input-output examples in the prompt to guide the model’s behavior.
A technique that prompts the model to solve problems through intermediate reasoning steps before producing a final answer.
A generalization of chain-of-thought that explores multiple reasoning paths in parallel, often using search/backtracking strategies.
A method that retrieves relevant documents at query time and incorporates them into the model’s response to improve accuracy and reduce hallucinations.
A cybersecurity attack that manipulates machine learning models through malicious prompts to cause unintended behavior.
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