We present a question-and-answer (Q\&A) application designed to support the contract management process by leveraging combined information from contract documents (PDFs) and data retrieved from contract management systems (database). This data is processed by a large language model (LLM) to provide precise and relevant answers. The accuracy of these responses is further enhanced through the use of Retrieval-Augmented Generation (RAG), text-to-SQL techniques, and agents that dynamically orchestrate the workflow. These techniques eliminate the need to retrain the language model. Additionally, we employed Prompt Engineering to fine-tune the focus of responses. Our findings demonstrate that this multi-agent orchestration and combination of techniques significantly improve the relevance and accuracy of the answers, offering a promising direction for future information systems.

We've long been fascinated with the idea of smart robots that serve human society. Think about Isaac Asimov's "I, Robot" short stories, Robby in the film "Forbidden Plant," and R2D2 or BB8 in "Star Wars." In these works, robots have some significant advantages over people. They are highly intelligent without being ruled by emotions. They act according to logical if-then processes.