AI-Driven Dynamic Conversational Retrieval Augmented Generation

Abstract

Rapid advances in the creation of large language models have dramatically impacted related fields, including natural language processing and text generation. However, these models often face limitations in connecting their outputs to accurate and up-to-date information, especially when retrieving facts from external sources. This limitation frequently results in inaccuracies, such as misstatements and errors.

The above-mentioned challenge will be addressed with the help of a web-based Software as a Service product using the Retrieval Augmented Generation framework. Such a framework augments the capabilities of a user to make chatbots retrieve accurate information and relevant knowledge from several sources such as PDF, Word document, and websites. Such an integrated process with the retrieval abilities greatly increases the reliability and functionality of the large language models.

Built on a modern technology stack that includes LangChain, Pinecone, TypeScript, OpenAI and Claude large language models, Next.js, PostgreSQL, Stripe, and Amazon Web Services, this platform offers scalable and innovative solution. This approach strengthens the grounding of outputs generated by large language models, so they are more accurate and contextually relevant, revolutionizing their application in various domains.