Prompt Engineering
| dc.contributor.author | Boonstra, Lee | |
| dc.date.accessioned | 2025-10-21T09:03:20Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | When thinking about a large language model input and output, a text prompt (sometimes accompanied by other modalities such as image prompts) is the input the model uses to predict a specific output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. However, crafting the most effective prompt can be complicated. Many aspects of your prompt affect its efficacy: the model you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context all matter. Therefore, prompt engineering is an iterative process. Inadequate prompts can lead to ambiguous, inaccurate responses, and can hinder the model’s ability to provide meaningful output. | |
| dc.identifier.citation | Boonstra L. Prompt Engineering. Google, 2025. 68 р. | |
| dc.identifier.uri | https://dspace.mipolytech.education/handle/mip/2809 | |
| dc.language.iso | en | |
| dc.publisher | ||
| dc.title | Prompt Engineering | |
| dc.type | Other |