Extracting Structured Information from Unstructured Text

Large Language Models (LLMs) excel at processing unstructured text and extracting structured information from it. This capability makes them well-suited for various information extraction tasks, such as identifying issues related to recent product releases based on user feedback.

Consider a scenario where a user reports an issue with updating the description of a product:

Subject: Unable to update product description
Description: After the latest update, I can no longer edit the description of my products. When I try to save the changes, I get an error message saying "Failed to update product description." This is preventing me from keeping my product information up to date. Please resolve this issue as soon as possible.

An LLM can analyze the user's report and cross-reference it with the release notes of the latest update. For example, the release notes might contain the following information:

Release Notes v3.2.1
- Added support for multiple product images
- Improved search functionality
- Fixed a bug causing product descriptions to be truncated
- New UI for editing product descriptions

By comparing the user's report with the release notes, the LLM can determine that the issue is likely related to the recent release, as a new UI for editing product descriptions was recently updated.

By leveraging LLMs to extract structured information from user feedback and cross-referencing it with release notes, organizations can quickly identify and prioritize issues related to recent product releases. This approach can help streamline the issue resolution process and improve overall user satisfaction.