Automating Data Entry with LLM: A Game Changer

Developments in the field of Language models has resulted in some tremendously useful API’s by OpenAI and other companies to provide users their models to build LLM applications.Lang Chain is a framework that helps to use these models in an organized and hygienic manner. In this article we talk about how data from documents can be extracted in structured format , this helps in automating the tasks of data entry and can help in making data validation faster.

Why Automate Data Entry?

Manually entering data from various documents is time-consuming and prone to errors. Automating this process not only saves time but also ensures that the data is accurate and consistent. This can be incredibly beneficial for teams in customer service, marketing, and other departments that rely heavily on data to make informed decisions.This also helps in improving quality of real time data analysis.

Extracting Data from Documents

The process of extracting data from documents involves a few key steps:

• Input Document: Start with a document, which could be a PDF, a web page, or any other text file.
• Define Schema: Specify the required fields, their data types, and descriptions to outline what data needs to be extracted.
• Initialize the Model: Use a language model like GPT-4 to create an extraction workflow.
• Data Processing: Break down the document into manageable chunks for easier…