AI Chatbot

Evolution from Rule-Based Systems

Early chatbots operated on keyword matching and scenario branching. They could answer questions like "What are your business hours?" but struggled with phrasings such as "How late are you open today?"—a limitation that had long been recognized as a problem.

The emergence of LLMs has significantly relaxed this constraint. Through language understanding capabilities acquired during pre-training, these models can respond flexibly to variations in expression. Furthermore, combining them with RAG makes it possible to generate accurate answers based on a company's own FAQs and product databases.

Multilingual Support in Practice

AI chatbots deliver particular value in multilingual environments. Taking Thailand's tourism industry as an example, visitors submit inquiries in a variety of languages—Japanese, English, Chinese, Korean, and more. Traditionally, the only options were to staff agents fluent in each language or to give up and offer English-only support.

An LLM-based chatbot can handle multiple languages within a single system. However, response quality varies by language; compared to English, which benefits from abundant training data, response accuracy tends to be lower for Thai and Lao. Measures such as incorporating domain-specific glossaries into RAG or supplementing with fine-tuning become necessary.

Anti-Patterns in Deployment

Projects that start with the mindset of "just connect the ChatGPT API and that's enough" frequently fail. There are many chatbot-specific considerations to address: countermeasures against hallucination, handling of personal information, and designing escalation (handoff to a human agent) flows. In environments subject to regulations such as the PDPA (Thailand's Personal Data Protection Act) in particular, it is essential to clearly define where users' input data is stored and processed.