Warranty data is a great example of where LLMs have evolved bureaucratic data overhead. What most people do not know is because of US federal TREAD regulation Automotive companies (If they want to land and look at warranty data) need to review all warranty claims, document, and detect any safety related issues and issue recalls all with an strong auditability requirement. This problem generates huge data and operations overhead, Companies need to either hire 10's if not hundreds of individuals to inspect claims or come up with automation to make this process easier.
Over the past couple of years people have made attempts with NLP (lets say standard ML workflows) but NLP and word temperature scores are hard to integrate into a reliable data pipeline much less a operational review workflow.
Enter LLM's, the world is a data gurus oyster for building an detection system on warranty claims. Passing data to Prompted LLM's means capturing and classifying records becomes significantly easier, and these data applications can flow into more normal analytic work streams.
Over the past couple of years people have made attempts with NLP (lets say standard ML workflows) but NLP and word temperature scores are hard to integrate into a reliable data pipeline much less a operational review workflow.
Enter LLM's, the world is a data gurus oyster for building an detection system on warranty claims. Passing data to Prompted LLM's means capturing and classifying records becomes significantly easier, and these data applications can flow into more normal analytic work streams.