Thank you for the advice! Regarding building a RAG, do mean making one based on, e.g., the this example? https://python.langchain.com/docs/expression_language/cookbo...

I wouldn’t waste my time learning about RAG. It’s quite likely GPT5 will make all current methods obsolete.

What would you recommend learning instead?

Learn whatever interests you the most.

What it is considered not to be a waste of time?

That’s not quite correct.

Private data especially in the enterprise cannot use public LLM’s like GPT-4 or 5 or N. Use cases needing data privacy have to use an internally implemented LLM application. In Currently, RAG is a concrete and pragmatic enterprise use of LLM’s aside from summarization, which is not amenable to using GPT-4.

GPT-5 may very well be amazing. But unless it runs on-prem it can’t be used in many scenarios because of data privacy.

To the OP - learning how to run LLM’s locally via say Ollama (see ollama.ai) will get you started in a hands on manner. See the /r/LocaLlama subreddit for a very active community around running LLM’s locally.

I think you’re missing my point. RAG is something that you currently implement yourself, or pay someone else who already implemented it. With GPT5 (or GPT6 at the latest), you just give it the same access you would give to a RAG system, and describe what you want it to do. It will do the rest.

Edit: I’m assuming the scenario where you do want to use the best model.