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PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars

Elvis Kimara, Kunle S. Oguntoye, Jian Sun

TL;DR

PersonaAI presents a scalable, privacy-conscious framework for creating personalized AI avatars by merging Retrieval-Augmented Generation with LLAMA models in a cloud-based mobile data pipeline. The approach uses 384-dimensional embeddings and recursive chunking to maintain contextual relevance, with dynamic top-$k$ retrieval and carefully engineered prompts to balance accuracy and uncertainty. Key contributions include efficient data ingestion, secure SaaS deployment, and a real-world-ready architecture demonstrated across general knowledge, recommendations, reminders, and writing-style tasks. The work advances personalized AI at scale, with potential impact across education, healthcare, and digital legacy domains, while addressing ethical considerations and user trust. The study also outlines practical future directions for multimodal data integration, domain adaptation, and stronger privacy-preserving mechanisms.

Abstract

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.

PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars

TL;DR

PersonaAI presents a scalable, privacy-conscious framework for creating personalized AI avatars by merging Retrieval-Augmented Generation with LLAMA models in a cloud-based mobile data pipeline. The approach uses 384-dimensional embeddings and recursive chunking to maintain contextual relevance, with dynamic top- retrieval and carefully engineered prompts to balance accuracy and uncertainty. Key contributions include efficient data ingestion, secure SaaS deployment, and a real-world-ready architecture demonstrated across general knowledge, recommendations, reminders, and writing-style tasks. The work advances personalized AI at scale, with potential impact across education, healthcare, and digital legacy domains, while addressing ethical considerations and user trust. The study also outlines practical future directions for multimodal data integration, domain adaptation, and stronger privacy-preserving mechanisms.

Abstract

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.

Paper Structure

This paper contains 27 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Related Works
  3. Experimental Platform
  4. Infrastructure and Equipment
  5. Algorithms and Data Structures
  6. Experimentation
  7. Deployment Strategy
  8. Methodology
  9. Data Preparation and Chunking Strategy
  10. Query Processing and Retrieval
  11. Language Model Integration and Prompt Engineering
  12. Datasets and Evaluation
  13. Building a Secure SaaS Framework
  14. Key Innovations
  15. Results
  16. ...and 12 more sections

Figures (4)

  • Figure 1: Recursive character chunking strategy with 200-character size and 0% overlap (demo).
  • Figure 2: Our simple website wit query and database page
  • Figure 3: Our AI can learn to respond like Spongebob
  • Figure 4: Our AI can respond like you