Prompt
İstem
D3
A prompt is input text or other input supplied at inference time to guide a generative model’s response for a task.
Review status: 2026-11-26
Technical explanation
A prompt can include instructions, examples, and supporting content for the current task, so its supplied input can shape the model’s output.
Conceptual boundaries
A prompt is request-time input rather than model training; in the cited Azure guidance, examples condition only the current inference rather than permanently changing the model.
Provider-neutral example
A support assistant can receive the prompt “Summarize this ticket in three bullets and flag missing account details.”
Limitations
Prompting can influence behavior, but prompt-engineering tests do not by themselves establish validity or reliability across heterogeneous use contexts.
Related concepts
Atomic claims and evidence
1.1NIST AI 100-2e2025, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
- Source
- NIST AI 100-2e2025, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
- Source role
- Authoritative source
- Exact locator
- Section 3.1.1 printed p. 38: In-context instructions and system prompts
- Supported claim
- In-context instructions supplied at inference time can shape LLM behavior, and prompts are used to align a model to an application-specific use case.
- Last verification
- Review due
- Scope limitation
- This describes inference-time prompting in LLM applications and does not claim that an instruction is always followed.
2.1NIST AI 600-1, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — D3 evidence slice
- Source
- NIST AI 600-1, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile — D3 evidence slice
- Source role
- Authoritative source
- Exact locator
- Appendix A.1.4 printed p. 50: prompt sensitivity and broad heterogeneity of contexts of use
- Supported claim
- Prompt-sensitive behavior varies across contexts of use, so prompting is not a guarantee of validity or reliability.
- Last verification
- Review due
- Scope limitation
- This is a risk statement about generative-AI use and does not measure any particular prompt or model.
3.1Microsoft Learn, Prompt engineering techniques for Azure OpenAI
- Source
- Microsoft Learn, Prompt engineering techniques for Azure OpenAI
- Source role
- Authoritative source
- Exact locator
- Examples; Supporting content; Few-shot learning
- Supported claim
- Microsoft’s Azure OpenAI prompt guidance presents instructions, few-shot examples, and supporting content as prompt components for a task and states that prompt examples condition only the current inference rather than permanently changing the model.
- Last verification
- Review due
- Scope limitation
- This is provider guidance for Azure OpenAI and does not prescribe a universal prompt format.