An intelligent low-code platform for building task-oriented LLM-based chatbots
Task-oriented chatbots help users complete specific goals such as ordering products. We recently proposed Taskyto, a domain-specific language (DSL) for building LLM-based task-oriented chatbots. Although Taskyto simplifies development, it remains challenging for non-experts due to complex installati...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:biblosearchi::042fe8330cdeb296412e0e5c88f5850b |
| Acceso en línea: | https://hdl.handle.net/10486/768361 https://dx.doi.org/10.1016/j.cola.2026.101401 |
| Access Level: | acceso abierto |
| Palabra clave: | Chatbots Intelligent assistants Low-code platforms Large Language Models Domain-specific languages Informática |
| Sumario: | Task-oriented chatbots help users complete specific goals such as ordering products. We recently proposed Taskyto, a domain-specific language (DSL) for building LLM-based task-oriented chatbots. Although Taskyto simplifies development, it remains challenging for non-experts due to complex installation, required DSL knowledge, and dedicated infrastructure to run the chatbots. To address these limitations, we present Bot-Craft, a low-code platform for creating and deploying Taskyto chatbots, augmented with an intelligent assistant that generates Taskyto code from natural language requests. We report on experiments demonstrating the assistant’s usability, effectiveness, performance, and accuracy in supporting chatbot development |
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