AI Systems

Prompt Engineering, explained

Prompt engineering is the practice of designing model inputs so an AI system behaves more reliably for a task.
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Part of the Semantic Notion technical glossary.

Short Definition

Prompt engineering is the practice of designing model inputs so an AI system behaves more reliably for a task.

Intuition

The prompt frames the job, constraints, context, and output format the model should follow.

Technical Definition

Prompt engineering includes instruction design, context placement, examples, tool descriptions, output schemas, and evaluation-driven iteration.

Example

A summarization prompt may define audience, length, source constraints, and forbidden speculation.

Common Misunderstandings

Prompt engineering is not a substitute for system design.
Prompts should be tested against real failure cases, not only happy paths.

Same Category

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Active ParametersActive parameters are the subset of a model's parameters used for a specific input, token, or inference step.AgentAn AI agent is a system that uses a model to choose actions, call tools, and make progress toward a goal over multiple steps.Fine-TuningFine-tuning adapts a pretrained model by continuing training on task-specific or domain-specific data.GuardrailA guardrail is a system control that constrains or monitors AI behavior to reduce unsafe, invalid, or undesired outputs.HallucinationA hallucination is model output that sounds plausible but is false, unsupported, or not grounded in the provided context.InferenceInference is the process of running a trained model on new inputs to produce outputs.

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AI EvaluationHow to Evaluate AI Systems in Production

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