Abstract

This paper presents Ready Tensor, a Retrieval-Augmented Generation (RAG)–based chatbot designed to enhance customer support systems through natural language understanding and intelligent document retrieval. The chatbot integrates retrieval-based search and generative AI models (like LLMs) to provide accurate, context-aware, and human-like responses. By connecting company knowledge bases and support FAQs with a generative model, Ready Tensor delivers quick, relevant answers to user queries, reducing response time and improving customer satisfaction.

Methodology

The methodology section outlines the system architecture and workflow of Ready Tensor:

Data Collection — Customer support tickets, FAQs, and product manuals are gathered as the knowledge base.

Text Preprocessing — Documents are cleaned, chunked, and embedded using models such as SentenceTransformers or OpenAI embeddings.

Retrieval System — A vector database (e.g., FAISS, Pinecone, or ChromaDB) is used to quickly find the most relevant text chunks to a user query.

Generative Model Integration — The retrieved context is passed to a Large Language Model (LLM) that generates a coherent, human-like response.

Evaluation Metrics — The chatbot’s performance is evaluated using response relevance, latency, and user satisfaction scores.

The model is implemented using Python, LangChain, and Ready Tensor’s open-source RAG framework.

Results

Experimental results show that the RAG-based chatbot performs significantly better than baseline FAQ bots in terms of:

Accuracy (retrieved correct answers in ~92% of test cases)

Response Quality (judged by human evaluators for coherence and helpfulness)

Response Speed (thanks to optimized vector search and caching).

User feedback highlighted improved satisfaction and reduced repetitive queries to human agents.