Explainable AI is more costly to build but the AI will be far more valuable than non-explainable AI. It is essential to understand the comes and goes of a problem instead of directly jump to the result without knowing why. I believe that AI will be a powerful source of information in the near future; full transparency and neutrality are required to make sure that the information will be reliable and unbiased.

Maybe also a kaggle club? 🤔

From my academic and work experience, RAG is great in a dynamic setting where you fetch the latest information from external source. But sometime, the data retrieved is biased, which can cause hallucinations in your model. This amazon blog has more detailed explanation about the reliability of RAG than I do: https://aws.amazon.com/blogs/machine-learning/evaluate-the-reliability-of-retrieval-augmented-generation-applications-using-amazon-bedrock/ As for fine-tuning, it is more specialized. You prepare your data, build your model, perform training and testing. This is great if you are working on an assignment, where you only have limited types of task to complete, but your model may be too specific and does not perform well on other types of task. If you combine both, have you heard about RAFT? (RAG + SFT = Retrieval Augmented Fine Tuning) You train your model on specific tasks while improving RAG performance. As an analogy, think about fine tuning as closed book exam, you studied beforehand and there is no extra learning while doing the exam (which means if you studied math but went to a biology exam, you messed up!); RAG would be open book exam but you did not study beforehand, so when you are faced with a question, you don't know the answer is in which book; and RAFT would be open book exam but you studied beforehand, so you know exactly which book has the answer to which question. The analogy is not exact, but it gets the idea.

I don't know whether should I be amazed by this technology or be sadden by its motivation