Offline LLM-Based Expert Systems for Confidential Software Documentation: Integrating Ollama and JSON Templates for Regulatory Adherence

This paper presents an offline expert system leveraging a locally deployed Large Language Model (LLM) via Ollama to automate secure, structured software project documentation. Operating entirely on-device, the system ensures data confidentiality while adhering to industry standards and user-customizable requirements. A novel JSON-formatted dataset fine-tunes the Ollama model to dynamically select context appropriate templates based on project type and inputs, streamlining compliance with regulatory frameworks. By eliminating cloud dependencies, the solution mitigates risks associated with third-party AI services, prioritizing privacy for sensitive projects. The framework demonstrates efficient offline document generation VS code IDE, balancing standardization with adaptability, and offers a scalable approach for secure.