Interaction via Large Language Models: Advancements in Retrieval-Augmented Intelligent Interfaces

Abstract

The rapid advancements in artificial intelligence, particularly in natural language processing, have enabled the development of systems that facilitate seamless interactions between humans and machines. This paper focuses on the design and implementation of intelligent interfaces that leverage Large Language Models (LLMs) to enable natural language-based interaction with websites. By integrating state-of-the-art tools such as Streamlit for creating dynamic interfaces, LangChain for prompt chaining and memory management, dotenv for secure configuration, and Ollama for LLM deployment, this project aims to deliver a user-centric, scalable, and efficient solution. Recent innovations in Retrieval-Augmented Generation (RAG) techniques are incorporated to enhance response accuracy and contextual relevance. The system demonstrates a 17.2% performance improvement over standard RAG implementations in multi-hop question scenarios, offering promising applications across diverse industries.

Introduction

Recent advancements in artificial intelligence, especially Large Language Models (LLMs), have transformed human-computer interaction by enabling natural language interfaces that simplify user interactions with websites. Traditional interfaces require complex navigation and technical knowledge, but LLM-powered conversational interfaces allow users to query websites directly using natural language, improving accessibility and user experience.

However, LLMs alone face challenges in accessing up-to-date or specific information not included in their training data. To address this, Retrieval-Augmented Generation (RAG) combines LLMs with information retrieval systems, enhancing accuracy and relevance. This paper presents the design and implementation of a modular system leveraging LLMs, RAG, and multiple technologies (Streamlit, LangChain, dotenv, Ollama) to enable scalable, secure, and user-friendly natural language interactions with websites.

Key motivations include overcoming the complexity of traditional websites, improving accessibility, and maintaining context in multi-turn conversations. The system architecture consists of modules for input processing, query refinement, data retrieval (via API or web scraping), vector-based semantic search (using Chroma), and response generation. Query refinement techniques include intent recognition, query expansion, decomposition, and contextual enrichment to handle complex user queries effectively.

A literature survey highlights existing approaches like chatbots, search engines, and RAG-based systems, noting their limitations in context retention, query complexity, and integration. Recent advancements in vector databases and RAG architectures improve retrieval quality but gaps remain in scalable integration and user experience design.

The implementation uses Python-based tools for rapid development, secure configuration, and local LLM deployment, with a focus on modularity, performance, and user-centric design. The paper aims to address challenges in natural language website interaction by combining state-of-the-art retrieval and generation methods within a flexible, extensible framework.

Conclusion

This paper presented a comprehensive approach to enabling natural language-based website interaction through the integration of Large Language Models and Retrieval-Augmented Generation techniques. By combining technologies such as Streamlit, LangChain, dotenv, and Ollama within a modular architecture, the system achieves significant improvements over existing solutions in terms of accuracy, contextual awareness, and user experience.The Implementation demonstrates the feasibility of creating intelligent interfaces that bridge the gap between natural language queries and website content, with applications across diverse industries and use cases. The advanced query refinement process, in particular, addresses a critical challenge in RAG systems by enhancing retrieval precision and supporting complex, multi-hop questions. Key contributions of this work include: 1) A modular, component-based architecture for integrating multiple technologies in LLM-powered interfaces. 2) Novel techniques for query refinement and decomposition that significantly improve retrieval effectiveness. 3) Empirical evidence of performance improvements compared to standard RAG implementations and other baseline approaches. 4) Insights into user interaction patterns and preferences when engaging with natural language interfaces. As LLM and RAG technologies continue to evolve, the potential for more sophisticated, context-aware, and personalized interactions with digital systems will expand. Future research should focus on addressing the identified limitations while exploring opportunities for multimodal integration, enhanced personalization, and more dynamic knowledge representation mechanisms.

References

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Copyright

Copyright © 2025 Atharv More, Dr. Mohammad Muqeem , Dr. Pawan Bhaldhare . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.