A Generalizable Agentic AI Pipeline for Developing Chatbots Using Small Language Models: A Case Study on Thai Student Loan Fund Services

The rising deployment of artificial intelligence in public services is constrained by computational costs and limited domain-specific data, particularly in multilingual contexts. This study proposes a generalizable Agentic AI pipeline for developing question–answer chatbot systems using small language models (SLMs), demonstrated through a case study on the Thai Student Loan Fund (TSLF). The pipeline integrates four stages: OCR-based document digitization using Typhoon2-3B, agentic question–answer dataset construction via a clean–check–plan–generate (CCPG) workflow, parameter-efficient fine-tuning with QLoRA on Typhoon2-1B and Typhoon2-3B models, and retrieval-augmented generation (RAG) for source-grounded responses. Evaluation using BERTScore and CondBERT confirmed high semantic consistency (F_BERT = 0.9807) and stylistic reliability (F_BERT = 0.9839) of the generated QA corpus. Fine-tuning improved the 1B model’s domain alignment (F_BERT: 0.8593 → 0.8641), while RAG integration further enhanced factual grounding (F_BERT = 0.8707) and citation transparency. Cross-validation with GPT-5 and Gemini 2.5 Pro demonstrated dataset transferability and reliability. The results establish that Agentic AI combined with SLMs offers a cost-effective, interpretable, and scalable framework for automating bilingual advisory services in resource-constrained government and educational institutions.