This project focuses on enhancing GPT models through Retrieval Augmented Generation (RAG). By integrating domain-specific retrieval mechanisms, we aim to customize large language models for targeted applications, improving accuracy, relevance, and contextual understanding.
The proposed framework follows the RAG methodology, comprising three key stages: retrieval of relevant information, analysis of user queries, and generation of appropriate responses. The following figure illustrates the complete framework, highlighting the interactions between the chatbot and its users, as well as the underlying processes involved in information retrieval, analysis, and response generation.
One of the distinctive features of this project, setting it apart from a basic chatbot, is its ability to show the retrieved documents after each Q&A session. This transparency allows users to see the exact sources from which the chatbot has derived its answers. The responses provided by the chatbot are directly retrieved from these documents, ensuring that the information is accurate and traceable.
The chatbot consistently provides accurate and relevant responses by drawing directly from the pre-loaded documents. By embedding the user query and searching the vector database for the most pertinent chunks, the system ensures that the answers are wellfounded. This approach minimizes the risk of generating speculative or incorrect information, which is a common issue with general-purpose generative AI models.
One of the key benefits of this chatbot is the transparency it offers. After each QA session, users can review the specific documents and sections from which the answers were derived. This feature builds trust, as users can verify the authenticity of the information provided. It also allows users to delve deeper into the source material if needed, promoting a more informed and thorough understanding of the topics discussed.
By strictly confining the chatbot’s responses to the content available in the training documents, we effectively mitigate the hallucination problem. This constraint is crucial in fields like cybersecurity, where accurate and reliable information is paramount. The chatbot’s design ensures that it does not generate fabricated answers, thereby maintaining a high standard of reliability.
Feedback from users indicates a high level of satisfaction with the chatbot’s performance. Users appreciate the ability to receive precise answers backed by specific documents. The feature that informs users when the chatbot lacks sufficient information to answer a query is particularly valued, as it prevents the dissemination of potentially misleading information.
Although the chatbot is designed with a focus on cybersecurity for ships, its architecture is adaptable to various other contexts. By loading different sets of documents, the chatbot can be repurposed for diverse ap- plications, ranging from healthcare to legal advice. This adaptability demonstrates the versatility of the RAG approach and its potential for broad application.
