对比低代码与自研方案，为教育类聊天机器人选型提供依据
LLM Chatbot-Creation Approaches
- 采用提示工程与RAG技术构建原型，评估性能与体验
- 低代码平台开发快但难扩展，自研系统控制强但需高技术能力
- 适合教育机构根据资源选择方案，未来探索混合架构
本文探讨基于大语言模型（如GPT-4、LLaMA）的课程聊天机器人开发策略，对比低代码平台（如AnythingLLM、Botpress）与自研方案（基于LangChain、FAISS、FastAPI）在教育场景中的应用。研究结合提示工程、检索增强生成（RAG）与个性化技术，评估原型在技术性能、可扩展性与用户体验方面的表现。结果表明：低代码平台支持快速原型设计，但在定制化与规模化方面存在局限；自研系统虽需较高技术门槛，但提供更强控制力。两种方法均成功实现自适应反馈与对话连续性。论文提出基于机构目标与资源的选型框架，未来将探索融合低代码便捷性与模块化自定义的混合方案，并引入多模态输入以支持智能辅导系统。
原文摘要 · Abstract (English)
This full research-to-practice paper explores approaches for developing course chatbots by comparing low-code platforms and custom-coded solutions in educational contexts. With the rise of Large Language Models (LLMs) like GPT-4 and LLaMA, LLM-based chatbots are being integrated into teaching workflows to automate tasks, provide assistance, and offer scalable support. However, selecting the optimal development strategy requires balancing ease of use, customization, data privacy, and scalability. This study compares two development approaches: low-code platforms like AnythingLLM and Botpress, with custom-coded solutions using LangChain, FAISS, and FastAPI. The research uses Prompt engineering, Retrieval-augmented generation (RAG), and personalization to evaluate chatbot prototypes across technical performance, scalability, and user experience. Findings indicate that while low-code platforms enable rapid prototyping, they face limitations in customization and scaling, while custom-coded systems offer more control but require significant technical expertise. Both approaches successfully implement key research principles such as adaptive feedback loops and conversational continuity. The study provides a framework for selecting the appropriate development strategy based on institutional goals and resources. Future work will focus on hybrid solutions that combine low-code accessibility with modular customization and incorporate multimodal input for intelligent tutoring systems.
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