Abstract
This study focuses on a solution approach that enhances contextual understanding and response generation by first encoding user inputs into high-dimensional embeddings. These embeddings are then used for efficient semantic search and retrieval of relevant context, which is integrated into prompts for the chat completion model that combines powerful semantic similarity capabilities with advanced language generation, enabling accurate and context-aware interactions. Potential enhancements include vector database integration for scalable storage and improved prompt engineering.
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Advanced, Technology, Data Science, Artificial-Intelligence, Machine Learning, Software
