Chatbots with some nuance. I work with voice and chat call centers hosted on Amazon Connect - the AWS version of the call center that Amazon uses internally.
Traditionally and still how it works in most call centers, you have to explicitly list out the things you can handle (intents), what sentences trigger them (utterances) and slots - ie “I want to get a flight from {origin} to {destination}” the variable parts would be the slots
Anyway, absolutely no company would or should trust an LLM to generate output to a customer. It never ends well. I use Gen ai to categorize free text input from a customer into a set of intents the system can handle and fill in the slots. But the output is very much on rails
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Chatbots with some nuance. I work with voice and chat call centers hosted on Amazon Connect - the AWS version of the call center that Amazon uses internally.
Traditionally and still how it works in most call centers, you have to explicitly list out the things you can handle (intents), what sentences trigger them (utterances) and slots - ie “I want to get a flight from {origin} to {destination}” the variable parts would be the slots
Anyway, absolutely no company would or should trust an LLM to generate output to a customer. It never ends well. I use Gen ai to categorize free text input from a customer into a set of intents the system can handle and fill in the slots. But the output is very much on rails
It works a lot better than the old school method.