PowerGPT™

AI-powered Chatbot Solution

What is PowerGPT™?

PowerGPT™, is an AI-powered chatbot, which is a software application designed to simulate human conversation using artificial intelligence technologies. These chatbots leverage natural language processing (NLP) and machine learning algorithms to understand and respond to user inquiries in real-time.

PowerGPT™ is a GPT-based and Context-aware search engine.

It is an innovative solution designed for internal use, aiming to enhance information retrieval within organizations. Unlike traditional keyword-based search engines, which often yield irrelevant results due to their reliance on specific terms, our context-aware search engine (PowerGPT™) offers a more intuitive approach. By utilizing a GPT-based model, our chatbot understands the nuances of user queries and provides accurate, context-rich responses. This advanced technology not only replaces the limitations of keyword-based searches but also improves the overall efficiency of information access inside your own database.

How does this Work?

PowerGPT™ is designed to elevate the effectiveness of internal data search, which mainly adopted below technologies:

It utilizes a GPT-based large language model (LLM) to understand user queries contextually, providing accurate and nuanced responses, unlike keyword-based search engines.

LLMs

It incorporates Retrieval-Augmented Generation (RAG) technology to enhance information retrieval, improving the efficiency of accessing organizational knowledge.

RAG

What are LLMs ?

LLMs are a type of deep learning model, specifically designed to understand, analyze, and generate human-like text. They leverage vast amounts of data to learn patterns, structures, and contexts in language, enabling them to perform tasks like text classification, sentiment analysis, summarization, translation, and more.

What is RAG?

RAG, or Retrieval Augmented Generation, is a technique that combines the capabilities of a pre-trained large language model with an external data source. This technique enhances the accuracy and reliability of generative AI models by allowing them to fetch factual information from external sources before generating responses. Key aspects of RAG include:

In conclusion, RAG enhances the capabilities of LLMs by ensuring that the generated output is not only original but also informed and applicable to real-world contexts. Below diagram shows how RAG works: