Software engineering builds on previous work, and the same goes for LLMs. Having worked with both enough to recognize the parallels, I find it interesting how many problems we now approach with general-purpose LLMs have roots in methods we have been using for years.

Sebastian Raschka’s piece on text classification is a useful reminder of that progression: bag-of-words representations with logistic regression or Naive Bayes, recurrent and convolutional networks, transformers, and now models positioned between specialized classifiers and general-purpose LLMs. The engineering question is therefore how much capability the workload actually requires.

For a bounded classification problem, model selection affects latency, inference cost, operational complexity and the amount of nondeterminism introduced into the system. A general-purpose LLM provides flexibility across tasks, while a specialized classifier can offer a narrower execution path when the input space and expected outputs are well defined.

My time with software engineering and data science has made this trade-off particularly familiar to me. The underlying principle has not changed much: understand the problem, define the constraints, then choose the least complex approach that satisfies them. LLMs expand the set of tools available to us, but they do not remove the architectural decisions that come before choosing one.

https://magazine.sebastianraschka.com/p/classifier-history-and-jev