Optimization of Large Language Models (LLMs) through Prompt Engineering
This article explored the impact of prompt engineering on optimizing the performance of large language models (LLMs) such as GPT and BERT. Prompt engineering was introduced as an innovative approach that involved designing specific instructions to guide the models' responses, enhancing their ac...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | Perú |
| Institución: | Universidad La Salle |
| Repositorio: | Revistas - Universidad La Salle |
| Idioma: | español |
| OAI Identifier: | oai:ojs.revistas.ulasalle.edu.pe:article/212 |
| Acceso en línea: | https://revistas.ulasalle.edu.pe/innosoft/article/view/212 https://doi.org/10.48168/innosoft.s24.a212 https://n2t.net/ark:/42411/s24/a212 |
| Access Level: | acceso abierto |
| Palabra clave: | Few-shot learning generative models LLMs prompt engineering zero-shot learning modelos generativos |
| Sumario: | This article explored the impact of prompt engineering on optimizing the performance of large language models (LLMs) such as GPT and BERT. Prompt engineering was introduced as an innovative approach that involved designing specific instructions to guide the models' responses, enhancing their accuracy and relevance without modifying their internal parameters. The study evaluated methodologies for constructing effective prompts, compared different strategies such as few-shot and zero-shot learning, and analyzed practical cases in areas like text generation, question answering, and sentiment analysis. The results demonstrated that a strategic design of prompts could significantly improve response quality, reduce errors, and expand the range of LLM applications. |
|---|