[Der_Einzige]

All the people saying "don't use fine-tuning" don't realize that most of traditional fine-tuning's issues are due to modifying all of the weights in your model, which causes catastrophic forgetting

There's tons of parameter efficient fine-tuning methods, i.e. lora, "soft prompts", ReFt, etc which are actually good to use alongside RAG and will likely supercharge your solution compared to "simply using RAG". The fewer parameters you modify, the more knowledge is "preserved".

Also, look into the Graph-RAG/Semantic Graph stuff in txtai. As usual, David (author of txtai) was implementing code for things that the market only just now cares about years ago.

[dmezzetti]

Thanks for the great insights on fine-tuning and the kind words!