Simply, the digital governance accelerates citizen-government service interaction more with simple tools. In the paper, a chatbot will be developed using web scraping techniques dynamically collecting and updating needed information from government websites based on eGovernance. This will facilitate meaningful, intuitive conversations between users and the language model, Llama 3.2, making the access to public data and services even more interesting. All this will be collected in real time by the web scraper: knowledge base of the chatbot, including policies issued, deadlines covered by the government, citizen services offered, and many more. Inserting language comprehension and conversational capabilities of Llama 3.2 with gathering data in real time would be expected to create ease of interaction for the user, with minimum delay in information, and a realistic answer to citizens' inquiries. Discussions also include issues when web scraping on public websites is applied, legal perspectives, and efficiency in the integration of large language models into eGovernance systems. Results are readily available, responsive, and satisfactory concerning user satisfaction for AI-powered government service chatbots. Index Terms-Chatbot, eGovernance, Llama 3.2, Natural Language Processing
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