Responsible Prompting Recommendation in Multi-Turn Interaction with LLMs
Resumo
Introdução: Large Language Models (LLMs) estão sendo propostos como solução para múltiplos fluxos de trabalho, mas eles comumente não contam com orientação adequada a usuários e nem fornecem informações sobre IA Responsável no momento da criação de prompts. Objetivo: Nesse contexto, esta pesquisa propõe uma forma de orientar e conscientizar pessoas sobre IA Responsável enquanto interagem com LLMs em múltiplos turnos. Metodologia ou Etapas: Em nossa ferramenta, usuários recebem sugestões de IA Responsável enquanto escrevem prompts para LLMs. Resultados: Espera-se que este trabalho motive mais sistemas de recomendação de prompting para promover IA Responsável no momento da criação dos prompts.
Palavras-chave:
IA Responsável, Inteligência Artifcial, Valores Sociais
Referências
Anthropic (2024). Promptgenerator. [link]. Accessed: 2025-08-21.
Arawjo, I., Swoopes, C., Vaithilingam, P., Wattenberg, M., and Glassman, E. L. (2024). Chainforge: A visual toolkit for prompt engineering and llm hypothesis testing. In Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI ’24, New York, NY, USA. Association for Computing Machinery.
Balas, M., Wong, D., and Arshinoff, S. (2024). Artificial intelligence, adversarial attacks, and ocular warfare. AJO International, 1(3).
Beauchemin, M. (2023). Mastering ai-powered product development: Introducing promptimize for test-driven prompt engineering. [link]. Accessed: 2025-08-21.
Bhatti, B. M. (2023). The art and science of crafting effective prompts for llms. [link]. Accessed: 2025-08-21.
Blodgett, S. L., Liao, Q. V., Olteanu, A., Mihalcea, R., Muller, M., Scheuerman, M. K., Tan, C., and Yang, Q. (2022). Responsible language technologies: Foreseeing and mitigating harms. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, CHI EA ’22, New York, NY, USA. Association for Computing Machinery.
Brade, S., Wang, B., Sousa, M., Oore, S., and Grossman, T. (2023). Promptify: Text-toimage generation through interactive prompt exploration with large language models. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, UIST ’23, New York, NY, USA. Association for Computing Machinery.
Chang, M., Druga, S., Fiannaca, A. J., Vergani, P., Kulkarni, C., Cai, C. J., and Terry, M. (2023). The prompt artists. In Proceedings of the 15th Conference on Creativity and Cognition, C&C ’23, page 75–87, New York, NY, USA. Association for Computing Machinery.
Character.AI (2024). Promptpoet. [link]. Accessed: 2025-08-21.
Cohere (2024). Prompttuner. [link]. Accessed: 2025-08-21.
Diab, M., Herrera, J., Sleep, M., Chernow, B., and Mao, C. (2022). Stable Diffusion Prompt Book. OpenArt.
Feng, L., Yen, R., You, Y., Fan, M., Zhao, J., and Lu, Z. (2024). Coprompt: Supporting prompt sharing and referring in collaborative natural language programming. In Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI ’24, New York, NY, USA. Association for Computing Machinery.
Fiannaca, A. J., Kulkarni, C., Cai, C. J., and Terry, M. (2023). Programming without a programming language: Challenges and opportunities for designing developer tools for prompt programming. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–7.
Gallery, D. (2022). DALL-E2 Prompt Book. Dallery Gallery.
Jiang, P., Rayan, J., Dow, S. P., and Xia, H. (2023). Graphologue: Exploring large language model responses with interactive diagrams. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, UIST ’23, New York, NY, USA. Association for Computing Machinery.
Kour, G., Zalmanovici, M., Zwerdling, N., Goldbraich, E., Fandina, O. N., Anaby-Tavor, A., Raz, O., and Farchi, E. (2023). Unveiling safety vulnerabilities of large language models. arXiv preprint arXiv:2311.04124.
Learn Prompting (2023). Prompt Engineering Guide.
Lo, L. (2023). The art and science of prompt engineering: A new literacy in the infomration age. Internet Reference Services Quarterly, (4):203–210.
Melanson, J. and Maman, B. (2023). Chatgpt +25 powerful ai tools 10x your productivity & creativity | chatgpt, generative ai, prompt engineering, dall-e2. E-learning Course. Perri, L. (2023). What’s new in artificial intelligence from the 2023 gartner hype cycle. Online.
PicFinder (2023). Picfinder. [link]. Accessed: 2025-08-21.
Maia Polo, F., Xu, R., Weber, L., Silva, M., Bhardwaj, O., Choshen, L., de Oliveira, A., Sun, Y., e Yurochkin, M. (2024). Efficient multi-prompt evaluation of llms. Advances in Neural Information Processing Systems, 37:22483–22512.
Promptomania (2023). Generic prompt builder. [link]. Accessed: 2025-08-21.
Rost, M. e Andreasson, S. (2023). Stable walk: An interactive environment for exploring stable diffusion outputs. Proceedings of the 4th Workshop on Human-AI Co-Creation with Generative Models HAI-GEN 23.
Santana, V. F. D. (2024). Challenges and opportunities for responsible prompting. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, CHI EA ’24, New York, NY, USA. Association for Computing Machinery.
Santana, V. F. d., Berger, S. E., Candello, H., Machado, T., Sanctos, C. S., Su, T., and Williams, L. (2025). Responsible prompting recommendation: Fostering responsible ai practices in prompting-time. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25, New York, NY, USA. Association for Computing Machinery.
Sarikas, O. (2023). Midjourney ai best prompt ticks beginners guide beginners mj explained nft art. YouTube.
Saxifrage (2023). Visual prompt builder. [link]. Accessed: 2025-08-21.
Simonovsky, T. (2023). Chatgpt for data science and data analysis in python. E-learning Course.
TensorFlow (2023). Embedding projector. [link]. Accessed: 2025-08-21.
Wang, Y., Shen, S., and Lim, B. Y. (2023). Reprompt: Automatic prompt editing to refine ai-generative art towards precise expressions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–29.
Weisz, J. D., Muller, M., He, J., e Houde, S. (2023). Toward general design principles for generative ai applications. [link]. Accessed: 2025-08-21.
Zamfirescu-Pereira, J., Wong, R. Y., Hartmann, B., and Yang, Q. (2023). Why johnny can’t prompt: How non-ai experts try (and fail) to design llm prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI ’23, New York, NY, USA. Association for Computing Machinery.
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., and Ba, J. (2022). Large language models are human-level prompt engineers.
Arawjo, I., Swoopes, C., Vaithilingam, P., Wattenberg, M., and Glassman, E. L. (2024). Chainforge: A visual toolkit for prompt engineering and llm hypothesis testing. In Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI ’24, New York, NY, USA. Association for Computing Machinery.
Balas, M., Wong, D., and Arshinoff, S. (2024). Artificial intelligence, adversarial attacks, and ocular warfare. AJO International, 1(3).
Beauchemin, M. (2023). Mastering ai-powered product development: Introducing promptimize for test-driven prompt engineering. [link]. Accessed: 2025-08-21.
Bhatti, B. M. (2023). The art and science of crafting effective prompts for llms. [link]. Accessed: 2025-08-21.
Blodgett, S. L., Liao, Q. V., Olteanu, A., Mihalcea, R., Muller, M., Scheuerman, M. K., Tan, C., and Yang, Q. (2022). Responsible language technologies: Foreseeing and mitigating harms. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, CHI EA ’22, New York, NY, USA. Association for Computing Machinery.
Brade, S., Wang, B., Sousa, M., Oore, S., and Grossman, T. (2023). Promptify: Text-toimage generation through interactive prompt exploration with large language models. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, UIST ’23, New York, NY, USA. Association for Computing Machinery.
Chang, M., Druga, S., Fiannaca, A. J., Vergani, P., Kulkarni, C., Cai, C. J., and Terry, M. (2023). The prompt artists. In Proceedings of the 15th Conference on Creativity and Cognition, C&C ’23, page 75–87, New York, NY, USA. Association for Computing Machinery.
Character.AI (2024). Promptpoet. [link]. Accessed: 2025-08-21.
Cohere (2024). Prompttuner. [link]. Accessed: 2025-08-21.
Diab, M., Herrera, J., Sleep, M., Chernow, B., and Mao, C. (2022). Stable Diffusion Prompt Book. OpenArt.
Feng, L., Yen, R., You, Y., Fan, M., Zhao, J., and Lu, Z. (2024). Coprompt: Supporting prompt sharing and referring in collaborative natural language programming. In Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI ’24, New York, NY, USA. Association for Computing Machinery.
Fiannaca, A. J., Kulkarni, C., Cai, C. J., and Terry, M. (2023). Programming without a programming language: Challenges and opportunities for designing developer tools for prompt programming. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–7.
Gallery, D. (2022). DALL-E2 Prompt Book. Dallery Gallery.
Jiang, P., Rayan, J., Dow, S. P., and Xia, H. (2023). Graphologue: Exploring large language model responses with interactive diagrams. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, UIST ’23, New York, NY, USA. Association for Computing Machinery.
Kour, G., Zalmanovici, M., Zwerdling, N., Goldbraich, E., Fandina, O. N., Anaby-Tavor, A., Raz, O., and Farchi, E. (2023). Unveiling safety vulnerabilities of large language models. arXiv preprint arXiv:2311.04124.
Learn Prompting (2023). Prompt Engineering Guide.
Lo, L. (2023). The art and science of prompt engineering: A new literacy in the infomration age. Internet Reference Services Quarterly, (4):203–210.
Melanson, J. and Maman, B. (2023). Chatgpt +25 powerful ai tools 10x your productivity & creativity | chatgpt, generative ai, prompt engineering, dall-e2. E-learning Course. Perri, L. (2023). What’s new in artificial intelligence from the 2023 gartner hype cycle. Online.
PicFinder (2023). Picfinder. [link]. Accessed: 2025-08-21.
Maia Polo, F., Xu, R., Weber, L., Silva, M., Bhardwaj, O., Choshen, L., de Oliveira, A., Sun, Y., e Yurochkin, M. (2024). Efficient multi-prompt evaluation of llms. Advances in Neural Information Processing Systems, 37:22483–22512.
Promptomania (2023). Generic prompt builder. [link]. Accessed: 2025-08-21.
Rost, M. e Andreasson, S. (2023). Stable walk: An interactive environment for exploring stable diffusion outputs. Proceedings of the 4th Workshop on Human-AI Co-Creation with Generative Models HAI-GEN 23.
Santana, V. F. D. (2024). Challenges and opportunities for responsible prompting. In Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, CHI EA ’24, New York, NY, USA. Association for Computing Machinery.
Santana, V. F. d., Berger, S. E., Candello, H., Machado, T., Sanctos, C. S., Su, T., and Williams, L. (2025). Responsible prompting recommendation: Fostering responsible ai practices in prompting-time. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25, New York, NY, USA. Association for Computing Machinery.
Sarikas, O. (2023). Midjourney ai best prompt ticks beginners guide beginners mj explained nft art. YouTube.
Saxifrage (2023). Visual prompt builder. [link]. Accessed: 2025-08-21.
Simonovsky, T. (2023). Chatgpt for data science and data analysis in python. E-learning Course.
TensorFlow (2023). Embedding projector. [link]. Accessed: 2025-08-21.
Wang, Y., Shen, S., and Lim, B. Y. (2023). Reprompt: Automatic prompt editing to refine ai-generative art towards precise expressions. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, pages 1–29.
Weisz, J. D., Muller, M., He, J., e Houde, S. (2023). Toward general design principles for generative ai applications. [link]. Accessed: 2025-08-21.
Zamfirescu-Pereira, J., Wong, R. Y., Hartmann, B., and Yang, Q. (2023). Why johnny can’t prompt: How non-ai experts try (and fail) to design llm prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI ’23, New York, NY, USA. Association for Computing Machinery.
Zhou, Y., Muresanu, A. I., Han, Z., Paster, K., Pitis, S., Chan, H., and Ba, J. (2022). Large language models are human-level prompt engineers.
Publicado
08/09/2025
Como Citar
SANTANA, Vagner Figueredo de; VAITHINATHAN ARAVINDAN, Ashwath; SAMPAIO SANCTOS, Cassia; MACHADO, Tiago; SOARES DE SOUZA, Luan.
Responsible Prompting Recommendation in Multi-Turn Interaction with LLMs. In: PÔSTERES E DEMONSTRAÇÕES - SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 24. , 2025, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 152-158.
DOI: https://doi.org/10.5753/ihc_estendido.2025.13182.
