Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.15489 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909544879226880 |
|---|---|
| author | Kimara, Elvis Oguntoye, Kunle S. Sun, Jian |
| author_facet | Kimara, Elvis Oguntoye, Kunle S. Sun, Jian |
| contents | This paper introduces PersonaAI, a cutting-edge application that leverages Retrieval-Augmented Generation (RAG) and the LLAMA model to create highly personalized digital avatars capable of accurately mimicking individual personalities. Designed as a cloud-based mobile application, PersonaAI captures user data seamlessly, storing it in a secure database for retrieval and analysis. The result is a system that provides context-aware, accurate responses to user queries, enhancing the potential of AI-driven personalization.
Why should you care? PersonaAI combines the scalability of RAG with the efficiency of prompt-engineered LLAMA3, offering a lightweight, sustainable alternative to traditional large language model (LLM) training methods. The system's novel approach to data collection, utilizing real-time user interactions via a mobile app, ensures enhanced context relevance while maintaining user privacy. By open-sourcing our implementation, we aim to foster adaptability and community-driven development.
PersonaAI demonstrates how AI can transform interactions by merging efficiency, scalability, and personalization, making it a significant step forward in the future of digital avatars and personalized AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars Kimara, Elvis Oguntoye, Kunle S. Sun, Jian Human-Computer Interaction Artificial Intelligence This paper introduces PersonaAI, a cutting-edge application that leverages Retrieval-Augmented Generation (RAG) and the LLAMA model to create highly personalized digital avatars capable of accurately mimicking individual personalities. Designed as a cloud-based mobile application, PersonaAI captures user data seamlessly, storing it in a secure database for retrieval and analysis. The result is a system that provides context-aware, accurate responses to user queries, enhancing the potential of AI-driven personalization. Why should you care? PersonaAI combines the scalability of RAG with the efficiency of prompt-engineered LLAMA3, offering a lightweight, sustainable alternative to traditional large language model (LLM) training methods. The system's novel approach to data collection, utilizing real-time user interactions via a mobile app, ensures enhanced context relevance while maintaining user privacy. By open-sourcing our implementation, we aim to foster adaptability and community-driven development. PersonaAI demonstrates how AI can transform interactions by merging efficiency, scalability, and personalization, making it a significant step forward in the future of digital avatars and personalized AI. |
| title | PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2503.15489 |