I don't think it will make enough difference, but RAG stands for Retrieval Augmented Generation.

There's a few ways to do it, but basically it's a way add extra information to the conversation. By default the model only knows what it generates, plus what is in the conversation. RAG adds extra information to the mix.

The simplest approach is to scan the conversation for keywords and add information based on them.

So you ask "what is the capital of France" and instead of the model answering/hallucinating by itself, your app could send the full Wikipedia page for France along with your question, and the model will almost always return the correct answer from the Wikipedia page and hallucinate much less. In practice it gets a lot more complicated and I'm not up to date on recent RAG but the idea is the same.

It's a chat bot that googles your question before answering in the hopes to cut down on hallucinations. It doesn't solve this problem at all.

A separate subsystem for an AI chatbot that allows it to get related to the user input information from text files(database) without scanning it all each time or having as an input to the promt, thus reducing hallucinations since instead of telling you something "from the head" it has an input in the form like this: user_input+info_content+memory.

Despite RAG being really helpful in many ways it doesn't eliminate hallucinations completely. Only lowers them to some point.