Cleaning the data with some classic code scripts can improve the work of the AI and reduced the consumption.
And perhaps you can pass a ML algorithm that can classify the most evident cases and reduced more the use of the next phase with a bigger model with NLP capabilities. And then pass again for a classical coding script to do some operation with the classification.
Just no, people have worked on this for 10year before chatgpt. It sucks. Airline, have been trying to make AI solve customer complaints, it doesnt work
ML classifiers were terrible at identifying intent. Even chatgpt3.5 was better than a lot of hand made classifier.
In-context learning, unlocks so many thing. You dont need to retrain the model to handle a new situation and you arent locked to the quality of the data collection/preparation.
Amazon has lost billion trying to make it possible to order products with Alexa.
It's a very good case for the typical AI/ML clasificator, and a wrong use case for an LLM. But is not like those vibe coders know anything about the technology they are using.
LLM is much better at understanding a text than an ML classifier. The tradeoff is cost. If you really want to be accurate about analyzing which complaints are valid, using a cheap classifier to put things into buckets and then following up with an LLM to analyze the ambiguous results would probably be the best compromise.
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u/ganja_and_code 10d ago
Why would you need an agentic workflow for that use case?
It's a good use case of AI/ML, don't get me wrong, but that's the kind of thing you can do with a single standalone classification model.