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Likewise, we've been building conversational interfaces for stuff like this for over a decade using traditional NLP techniques and orchestration rather than LLM
by DebtDeflation 1y ago
Likewise, we've been building conversational interfaces for stuff like this for over a decade using traditional NLP techniques and orchestration rather than LLMs. Intent classification, named entity extraction, slot filling, and API calling.
Processing returns is pretty standardized - identify the order and the item within it being returned, capture the reason for the return, check eligibility, capture whether they want a refund or a new item, and execute either. When you have a fully deterministic workflow with 5-6 steps, maybe 1 or 2 if-then branches, and then a single action at the end, I don't see the value of running an LLM in a loop, burning a crazy amount of tokens, and hoping it works at least 80% of the time when there are far simpler and cheaper ways of doing it that will work almost 100% of the time.
- zsyllepsis 1y agoTrue, we have been building conversational interfaces with traditional NLP. In my experience, they’ve been fairly fragile. Extending the example you gave, nicely packaged, fully deterministic workflows work great in demos. Then customers start going off the paved path. They ask about returning 3 items all at once, or a whole order. They get confused and provide a shipping number instead of an order number. They switch language part of the way through the conversation because they get frustrated by all these follow-up questions. All of these absolutely can be handled through traditional NLP, but require system designers to account for them, model the conversation, and design their system accordingly to react accordingly. And suddenly the 5-6 step deterministic workflows with a couple of if-branches… isn’t.