In this context they are more like conversational search engines. But that’s a pretty decent feature IMO.
Note too that these models can, and do, make up references. If it predicts a reference is called for, then it'll generate one, and to the LLM it makes no difference if that reference was something actually in the training data or just something statistically plausible it made up.
If you pay for it, ChatGPT can spend upwards of 5 minutes going out and finding you sources if you ask it to.
Those sources can than be separately verified, which is up to the user - of course.
The net result is that some responses are going to be more reliable (or at least coherently derived from a single search source) than others, but at least to the casual user, maybe to most users, it's never quite clear what the "AI" is doing, and it's right enough, often enough, that they tend to trust it, even though that trust is only justified some of the time.
And, don’t argue with me about terms. It literally stands for retrieval (not store or delete or update) augmented generation. And as generation is implied with LLMs it really just means augmenting with retrieval.
But if you think about it the agent could be augmented with stores or updates as well as gets, so that’s why it’s not useful, plus nobody I’ve seen using RAG diagrams EVER show it as an agent tool. It’s always something the system DOES to the agent, not the agent doing it to the data.
So yeah, stop using it. Please.