LLM Portfolio Bot
RAG based AI representative for creators
The potential of AI to enhance creative applications is huge, yet it often remains largely accessible to tech giants and startups with clear project-market fits. For individual creators and artists, the high computing costs and technical barriers of hosting and developing large language model (LLM)-based works present significant challenges. This project explores how LLMs can be leveraged on a smaller, more personal scale, transforming them into powerful creative tools tailored to individual needs.
As part of this exploration, I developed an LLM-based portfolio chatbot using a Retrieval-Augmented Generation (RAG) approach. The chatbot retrieves personal information and portfolio content from a markdown-based rich media database and runs on a compact 8B LLM model locally, hosted on a Google Colab node. By employing a tailored set of prompt templates, the chatbot serves as a virtual representative capable of answering inquiries about my work in a visually rich and stylistically engaging manner. Key features include:
Cost-Effective Local Deployment: Hosting smaller LLM models with personal data on personal-level servers ensures accessibility for individual creators.
Rich Media Integration: The chatbot can retrieve and display images from the database, showcasing work visually alongside textual responses.
Personalized, Stylistic Responses: Using LangChain’s LLM Chain, the chatbot provides accurate and engaging answers through role-playing prompt templates.
By enabling creators to access AI on an individual scale, this chatbot attempt to challenge the dominant narrative of AI as solely a large-scale industrial tool, demonstrating its potential to foster personalized and meaningful interactions.
