Prompt Engineering & System Prompts
Design of natural-language instructions, examples, constraints, and tool schemas that steer foundation-model behavior at inference time.
Core metadata
- ID: prompt_engineering_system_prompts
- Era: Modern
- First known date: 2017 (decade)
- Region: Global / multiple regions
- Review status: source_checked
- Maturity: established
Prerequisites
- Large Language Models (large_language_models)
- Computational Linguistics (linguistics_computational)
- Software Engineering (software_engineering)
Dependents
- None.
Fields
Field lanes
- Artificial Intelligence & Machine Learning: Foundation Models
Node sources
- Language Models are Few-Shot Learners (arXiv, 2020, primary_paper) • Supports: node, maturity
- Aligning Language Models to Follow Instructions (OpenAI, 2022, generic_overview) • Supports: node, maturity
Prerequisite edge evidence
Edge/source evidence summary:
- Prerequisite edges: 3
- Average edge confidence: 68%
- Prerequisite sources: 3
- expert_inference: 3
| Prerequisite | Type | Confidence | Evidence level | Note | Sources |
|---|---|---|---|---|---|
| Large Language Models (large_language_models) | enabling | 68% | expert_inference | Large Language Models provides a capability that enables this technology without being the only possible path. |
|
| Software Engineering (software_engineering) | enabling | 68% | expert_inference | Software Engineering provides a capability that enables this technology without being the only possible path. |
|
| Computational Linguistics (linguistics_computational) | enabling | 68% | expert_inference | Computational Linguistics provides a capability that enables this technology without being the only possible path. |
|
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