By the end of Module 2, learners will understand various methods for implementing LLMs in accounting, including UI, API, UI-RPA, and API-RPA, and be able to evaluate their advantages and limitations. They will develop the ability to choose the most suitable implementation approach for different accounting tasks while considering key integration factors. Additionally, they will gain insights into practical considerations and make informed decisions about LLM adoption based on organizational needs and available resources.

By the end of Module 3, learners will understand the challenges of extracting financial data from unstructured sources and explore the components and workflow of an LLM-enabled data extraction framework. They will learn how to apply prompt engineering techniques to enhance extraction accuracy and recognize how the framework can be adapted for various financial documents. Additionally, they will appreciate the efficiency and accuracy benefits that LLMs bring to financial data extraction.

By the end of Module 4, learners will be able to evaluate the accuracy and efficiency of an LLM-enabled data extraction framework and interpret its results across different financial documents. They will identify common extraction errors and apply strategies to address them while refining prompts to enhance performance. Additionally, they will explore considerations for scaling the framework to handle larger datasets and different LLMs effectively.