Before the rise of Large Language Models (LLMs), conversational chatbots were designed with rigid flowcharts that mapped out every conversational step, predicted user responses, and scripted specific outcomes.
Teams spent millions creating these flows, hiring conversation designers to generate thousands of utterances, and training natural language processing [NLP] classifiers to guide users along the correct routes.
However, this converted only a minimal number of user queries into successful support, and more often than not,…
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