100% this web page (or similar) will be used to basically scam clients into overpaying for simple wrappers around llama_index or LangChain etc. Some people will spend a week wasting their time trying to fine-tune an open source LLM on some wholly inadequate dataset before realizing they can use OpenAI and something from github. But most will not admit that.
Sure, a few people doing basically research projects for a large company or university will find some of the information useful. But realistically, probably not so much if they have to actually deliver a working system in a reasonable amount of time that would justify the business expense.
I'll grant you that the way the author presents his ideas seems a bit academic, but I assure you all of this information is just the immediate things you run into as a software engineer trying to integrate LLM's into your systems. No one is jumping to finetuning before they've even tried GPT4 on their problem.
Source: finished a chat to your documents project a month ago
Finetuning is not that needed in my experience.
rsync
On the other hand, directly using an off-the-shelf model, even the best ones, may not meet your performance requirements.
That’s where fine-tuning an open LLM is necessary.
Practically speaking the starting point should be things like the APIs such as OpenAI or open source frameworks and software. For example, llama_index https://github.com/jerryjliu/llama_index. You can use something like that or another GitHub repo built with it to create a customized chatbot application in a few minutes or a few days. (It should not take two weeks and $15,000).
It would be good to see something detailed that demonstrates an actual use case for fine tuning. Also, I don't believe that the academic tests are appropriate in that case. If you really were dead set on avoiding a leading edge closed LLM, and doing actual fine-tuning, you would want a person to look at the outputs and judge them in their specific context such as handling customer support requests for that system.