Prompt engineering
İstem mühendisliği
D3
Prompt engineering is the iterative design and refinement of instructions and context used to guide a model’s response for a task.
Review status: 2026-11-26
Technical explanation
It can specify a task, output format, examples, constraints, and supplied context, then test and revise those inputs against representative cases.
Conceptual boundaries
Prompt engineering is not fine-tuning because it changes inference-time input rather than training model parameters, and it is not a guarantee of performance.
Provider-neutral example
A team can compare two instructions for extracting invoice fields and retain the version that is clearer on a representative, reviewed sample.
Limitations
Prompt changes can overfit to examples, conflict with other instructions, or fail in edge and adversarial cases, so evaluation must include representative and adversarial cases.
Related concepts
Atomic claims and evidence
1.1Microsoft Learn, Prompt engineering techniques for Azure OpenAI
- Source
- Microsoft Learn, Prompt engineering techniques for Azure OpenAI
- Source role
- Authoritative source
- Exact locator
- Scenario-specific guidance; Few-shot learning; Best practices
- Supported claim
- Azure prompt engineering changes request-time prompt content, whereas Azure fine-tuning trains on more examples than fit in a prompt and adapts model weights to a task.
- Last verification
- Review due
- Scope limitation
- This compares Azure implementation approaches and does not establish performance for every prompt or fine-tuning job.
1.2Microsoft Learn, Customize a model with fine-tuning
- Source
- Microsoft Learn, Customize a model with fine-tuning
- Source role
- Authoritative source
- Exact locator
- In this article: fine-tuning trains on more examples than fit in a prompt and adapts model weights to the task
- Supported claim
- Azure prompt engineering changes request-time prompt content, whereas Azure fine-tuning trains on more examples than fit in a prompt and adapts model weights to a task.
- Last verification
- Review due
- Scope limitation
- This compares Azure implementation approaches and does not establish performance for every prompt or fine-tuning job.
2.1Microsoft Learn, System message design for Azure OpenAI
- Source
- Microsoft Learn, System message design for Azure OpenAI
- Source role
- Authoritative source
- Exact locator
- Design checklist: Test, measure, and iterate; Common pitfalls; Limitations
- Supported claim
- Microsoft’s Azure OpenAI system-message guidance recommends realistic and adversarial testing because instructions can overfit to examples or fail in edge cases, and conflicting instructions can change behavior.
- Last verification
- Review due
- Scope limitation
- This is provider guidance for Azure OpenAI system messages and does not predict every other implementation’s behavior.