This chapter presents an advanced AI-driven framework designed to enhance Large Language Models (LLMs) and Machine Learning methodologies through rule-based augmentation, optimizing diagnostic decision-making in Primary Care. The proposed approach introduces a structured methodology, depicted via a process diagram, which defines the integration of generative AI functions within a rule-enriched paradigm. To achieve this, the framework decomposes generative AI workflows into modular, functional segments, supporting the development of an implementation-oriented data flow model. A pivotal element of this system is the interaction model, which establishes the theoretical foundation for user engagement within an AI-assisted medical application. Named “Med|Primary AI Assistant,” this tool aids users by interpreting symptoms and generating diagnostic guidance. The framework leverages cutting-edge Natural Language Processing (NLP) advancements to create a domain-specific knowledge structure, coupled with a systematic mechanism for generating contextually relevant medical insights. Beyond structuring AI-driven interactions, this methodology integrates a dynamic content adaptation mechanism, ensuring personalized and context-aware engagement tailored to user needs. Additionally, a rule-based evaluation strategy-grounded in contextual analysis and dialogue theory-provides an algorithmic framework for assessing content precision and response quality within the AI-powered diagnostic assistant.