Large language model integrated AI diet planning tool with macro nutrient calculations

Contemporary technological advancements have facilitated the implementation of artificial intelligence and large language models (LLMs) for addressing individual health and nutritional requirements. This research examines the application of various LLMs including Llama 3.1-70B, GPT-3.5, and GPT-4 for generating personalized dietary recommendations. The system calculates macro-nutrient requirements based on user-specific data and interfaces with selected LLMs to produce customized meal plans. The application architecture comprises a Flutter-based front-end and LLM API integration, processing user inputs and formatting responses according to predefined templates. This research, encompasses the comparative analysis of different LLMs regarding performance and cost-effectiveness, the development of macro-nutrient calculation algorithms, and the evaluation of prompt engineering techniques. Additionally, it assesses the application's practical viability in real-world nutritional contexts. By integrating AI technologies with nutritional programming, this research aims to automate personalized diet planning and enhance individual health outcomes through technological intervention.