
Experience working with neural networks as one of the key tasks has identified the need for the correct formulation of text queries in order to obtain relevant results. This study aims to use general scientific (classification, specification, generalization, synthesis) and empirical (observation, description, comparison) research methods to study general recommendations on the process of creating projects and analyze the possibilities of online assistants for its successful implementation.
Опыт работы с нейросетями в качестве одной из ключевых задач обозначил необходимость корректной формулировки текстовых запросов для получения релевантных результатов. В данном исследовании ставится целью при помощи общенаучных (классификация, конкретизация, обобщение, синтез) и эмпирических (наблюдение, описание, сравнение) методов исследования изучить общие рекомендации по процессу создания промтов и проанализировать возможности онлайн-помощников для его успешной реализации.
PROMT, SERVICE ASSISTANT, НЕЙРОСЕТЬ, СЕРВИС-ПОМОЩНИК, КОРРЕКТНАЯ ФОРМУЛИРОВКА, NEURAL NETWORK, ПРОМТ, CORRECT FORMULATION
PROMT, SERVICE ASSISTANT, НЕЙРОСЕТЬ, СЕРВИС-ПОМОЩНИК, КОРРЕКТНАЯ ФОРМУЛИРОВКА, NEURAL NETWORK, ПРОМТ, CORRECT FORMULATION
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
