Chatbot for recipe videos

Abstract

This report explores advancements and methodologies in the development and fine-tuning of large language models (LLMs) and their integration into knowledge-intensive tasks. It em phasizes the role of retrieval-augmented generation (RAG) and low-rank adaptation (LoRA) techniques in optimizing the efficiency and accuracy of LLMs. Key applications, including personalized dialogue systems, recipe generation, and chatbot frameworks, are highlighted to demonstrate the practical implications of these approaches. Additionally, the report evaluates dynamic context integration and prompt engineering strategies, offering insights into their im pact on improving conversational AI systems. By leveraging cutting-edge research and tools like Meta’s LLAMA model and the RecipeNLGdataset, this work provides a comprehensive overview of current trends and innovations in natural language processing (NLP). Recommendations for future development, including parameter-efficient fine-tuning and real-time problem-solving ca pabilities, are proposed to guide ongoing advancements in this rapidly evolving field.