Optimizing Web-Based AI Query Retrieval with GPT Integration in LangChain A CoT-Enhanced Prompt Engineering Approach

TL;DR

This paper presents a novel method for improving remote learning resource retrieval by integrating GPT models with LangChain, utilizing CoT reasoning and prompt engineering to enhance relevance and contextual understanding.

Contribution

It introduces a new framework combining GPT, LangChain, and CoT reasoning to improve the precision and contextual relevance of educational resource retrieval.

Findings

Enhanced retrieval accuracy and relevance.

Improved user satisfaction and learning outcomes.

Effective integration of GPT with LangChain for education.

Abstract

Large Language Models have brought a radical change in the process of remote learning students, among other aspects of educative activities. Current retrieval of remote learning resources lacks depth in contextual meaning that provides comprehensive information on complex student queries. This work proposes a novel approach to enhancing remote learning retrieval by integrating GPT-based models within the LangChain framework. We achieve this system in a more intuitive and productive manner using CoT reasoning and prompt engineering. The framework we propose puts much emphasis on increasing the precision and relevance of the retrieval results to return comprehensive and contextually enriched explanations and resources that best suit each student's needs. We also assess the effectiveness of our approach against paradigmatic LLMs and report improvements in user satisfaction and learning outcomes.