>And how much data can you give it?

Well, theoretically you can give it up to the context size minus 4k tokens, because the maximum it can output is 4k. In practice, though, its ability to effectively recall information in the prompt drops off. Some people have studied this a bit - here's one such person: https://gritdaily.com/impact-prompt-length-llm-performance/

> And how much data can you give it?

128,000 tokens, which is about the same as a decent sized book.

Their other models can also be fine-tuned, which is kinda unbounded but also has scaling issues so presumably "a significant percentage of the training set" before diminishing returns.

It is great for proof-reading text if you are not a native English speaker. Things like removing passive voice. Just give it your text and you get a corrected version out.

Use a cli tool to automate this from the cli. Ollama for local models, llm for openai.

People never talk about Gemini, and frankly it's output is often the worst of SOTA models, but it's 2M context window is insane.

You can drop a few textbooks into the context window before you start asking questions. This dramatically improves output quality, however inference does take much much longer at large context lengths.