Fine-tuning is not a good approach to integrating new knowledge into an LLM. It's a good way to drive the direction of the LLM's style, structure of responses (e.g., length, format).
I'd say RAG is still very much the way to go. What you need to then do is optimize how you chunk and embed data into the RAG database. Pinecone has a good post on this[1] and I believe others[2] are working on more automated solutions.
If you want a more generalized idea here, what state of the art (SOTA) models seems to be doing is using a more general "second brain" for LLMs to obtain information. This can be in the form of RAG, as per above, or in the form of more complex and rigorous models. For example, AlphaGeometry[3] uses an LLM combined with a geometry theorem prover to find solutions to problems.
[1] https://www.pinecone.io/learn/chunking-strategies/
[2] https://unstructured.io/
[3] https://deepmind.google/discover/blog/alphageometry-an-olymp...
