A Prompt Engineering Approach for Structured Data Extraction from Unstructured Text Using Conversational LLMs

This paper aims to extract structured information from unstructured text written in natural language. This extracted information can then be stored in a database and queried using database access languages such as SQL to derive meaningful answers to questions that might arise around a specific activity related to that unstructured text. For example, an email chain discussing a planned trip could be used to extract a record with fields such as who is taking the trip, where they are departing from, when they are departing, where they are going, and when they are arriving. This information could then be used to create a relation of trips that could be queried by SQL. This paper uses a prompt engineering approach using conversational LLMs for extracting the relevant information related to travel and store the information into relational databases which can then be queried using SQL or any other query language. This initiative holds the promise to revolutionize the storage and retrieval of information with minimal effort. Currently, unstructured text is difficult to query and analyze. By extracting structured information from this text, it becomes much easier to work with.