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Prompt engineering structures and refines prompts to elicit specified outputs from generative AI models.
Prompt engineering is the process of structuring and refining natural-language inputs (prompts) so a generative AI model produces specified, useful outputs. It involves understanding how the model interprets language and may include designing clearer instructions, adding relevant context, specifying the desired output style, and assigning roles or behaviors the model should mimic. The purpose of prompt engineering is to improve accuracy, relevance, and consistency, especially because model performance can be highly sensitive to small changes in wording, formatting, and example ordering. Common techniques include few-shot prompting (multi-shot), chain-of-thought prompting (often with self-consistency), and tree-of-thought prompting. For multimodal models, prompts also guide image generation by describing subject, medium, style, lighting, and other visual attributes. Prompt engineering is closely related to context engineering, which focuses on managing non-prompt inputs such as system instructions, retrieved knowledge, tool definitions, and metadata. In addition, automated prompt generation and optimization methods (e.g., retrieval-augmented generation, GraphRAG, and search/optimization over prompt strings or continuous “soft prompts”) can reduce manual trial-and-error. The field also includes security considerations such as prompt injection attacks, where malicious prompts attempt to manipulate model behavior.
Prompt engineering structures and refines prompts to elicit specified outputs from generative AI models.
It improves performance despite models being sensitive to small prompt changes, using techniques like few-shot, chain-of-thought, and tree-of-thought.
Context engineering complements prompt engineering by managing additional inputs (system instructions, retrieved knowledge, tools, metadata).
Automated methods (RAG/GraphRAG and prompt optimization/search) can generate or improve prompts with less manual effort.
Prompt injection is a security risk where malicious prompts target ML models to cause harmful or unintended behavior.
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 instructions, context, and desired output characteristics.
The practice of managing the non-prompt and prompt context supplied to a generative AI model (e.g., system instructions, retrieved knowledge, tool definitions, metadata) to improve reliability and efficiency.
A prompting approach that includes a small number of examples in the prompt so the model can learn the pattern in-context.
A technique that encourages the model to solve problems through intermediate reasoning steps before producing a final answer.
A method that runs multiple chain-of-thought rollouts and selects the most common conclusion among them.
A technique that explores multiple reasoning paths in parallel (often using search/backtracking) to improve solution quality.
A method that retrieves relevant documents at query time and uses them to ground the model’s response, improving accuracy and reducing hallucinations.
An extension of RAG that uses a knowledge graph to connect and synthesize information across large datasets.
A cybersecurity attack where malicious prompts are used to manipulate machine learning model behavior.
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