Take a Lexis Nexis as an example. They used to build sophisticated NLP pipelines to squeeze information out of legal documents: Part-of-Speech tagging, dependen

Take a Lexis Nexis as an example. They used to build sophisticated NLP pipelines to squeeze information out of legal documents: Part-of-Speech tagging, dependency parsing, NER, topic modeling, relation extraction, event extraction, summarization, and etc. They also spend millions of dollars on custom training just to expand the entity types and etc. The whole process is error prone, painful to maintain, and expensive. Similarly, McDonald must have struggled a lot building their automatic ordering system. LLM must be a godsend for these companies. All of sudden, low-level tasks like POS can be eliminated. Tasks like NER have only limited use and the companies can enjoy orders more entity types almost for free. Tasks like intention slotting and topic modeling become trivial compared to the pre-LLM-era pipelines.