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May 8, 2026Journal of Computer Languages1 citationsOpen Access

An intelligent low-code platform for building task-oriented LLM-based chatbots

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ÁQÁngel QuilesEGEsther GuerraJLJuan de Lara

Key Points

  • This research focuses on simplifying the development process of task-oriented chatbots for non-experts using Bot-Craft.
  • Developed a low-code platform, Bot-Craft, for creating Taskyto chatbots.
  • Integrated an intelligent assistant that translates natural language requests into Taskyto code.
  • Conducted experiments assessing usability, performance, and accuracy.
  • The intelligent assistant significantly improves usability and developer efficiency.
  • Performance metrics confirm the accuracy of the assistant in generating code from requests.
  • User feedback highlights substantial reductions in development time and complexity.

Abstract

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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Cite This Study

Quiles et al. (2026) studied this question.

synapsesocial.com/papers/69fd7ddcbfa21ec5bbf06093https://doi.org/10.1016/j.cola.2026.101401
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