Traditional machine learning (ML) has been reliably used for computational tasks due to its deterministic and reproducible nature. These models are excellent at handling numbers and structured outputs, which can then be leveraged by LLMs to ground responses in factual data, preventing LLM hallucinations.
The key approach is to use the best tool for the job—traditional ML for structured, explainable tasks and LLMs for generative tasks. Explainability is crucial, as traditional ML models are much easier to interpret than LLMs, making them valuable in contexts where transparency is required.
For more on explainability, see here.
For a deeper dive on combining traditional ML with LLMs, check out this blog.
How do gen AI and traditional AI complement each other? Check out this related question.