You train an addend to the weights rather than the weights themselves. You limit the dimensions[1] available in this addend to a small fraction of the dimensions of the weights, making training cheaper.

When you have a few example chats you want a model to emulate - say you made it from your proprietary data, you can train any open model on that data in this cheap way. You don't lose any quality versus not using lora since the models overall knowledge won't shift that much due to your data anyways, so it's a waste to make high dimensional updates.

However, only in some cases is it worth it and equal in quality to just making a good retrieval system and exposing it to claude code or whatever. If a retrieval system over the same data is very difficult, or if the data simply must be proprietary, then you should go for it.

[1] technically "rank", but I'm simplifying

Running an open model locally on your own machine, and having it augment-train itself using your own sessions as the training input.