Prompt Optimization for AI-Powered Recipe Generation: Challenges and Insights
Aadya Jha · International Journal for Research in Applied Science and Engineering Technology · 2024
In today's digital age, artificial intelligence (AI) is transforming the culinary landscape by enabling personalized recipe generation. AI-powered recipe generation systems can offer tailored cooking solutions based on user preferences, dietary restrictions, and ingredient availability. However, achieving optimal results remains challenging due to the inherent complexity of natural language processing (NLP) in generating coherent, contextually relevant recipes. This paper investigates the prompt engineering techniques employed to enhance the accuracy and creativity of AI-based recipe generation models. By exploring various prompt structures and model fine-tuning methods, this research highlights how subtle adjustments in prompt design can significantly influence the quality and relevance of generated recipes. The study utilizes a diverse dataset of ingredients, cuisines, and dietary requirements, and examines models including GPT-Neo and GPT-3. Key findings reveal that effective prompt optimization can improve recipe coherence, ingredient compatibility, and instruction clarity. Challenges encountered include managing model verbosity, reducing ingredient redundancy, and achieving cultural or cuisine-specific accuracy. This research underscores the importance of prompt engineering in refining AI-generated content within the culinary domain. Future work will focus on integrating user feedback to dynamically adjust prompts and exploring multimodal AI approaches for enhanced visual and textual recipe generation.