I don't think that's a dumb question at all. It depends on your objectives and how much resources you're willing to spend. These open weights models can be re

I don't think that's a dumb question at all. It depends on your objectives and how much resources you're willing to spend. These open weights models can be retrained. Start with a foundational model like Llama2 or something and expose it to more recent training data that includes whatever updated information you want it to have access to. This is relatively expensive, but allows for big changes to the model. If you have some relatively small subset of new information you want to bring in, you could build a Lora. Then either run your model with the Lora, or fold the Lora into your base model. This is relatively cheap, but fairly narrow in terms of your updates. In the long run, it might be that Retrieval Augmented Generation (RAG) is the way to go. Here, your embeddings go into a vector database, and the model reads from there. Then you just need to update the database for the model to have access to new information. This LLM stuff is new enough that anything like best practices are still being worked out. The optimal way to bring in new information could be a variant of one of the methods I mentioned above, or some combination of all three, or something else altogether.