There is a great 3blue1brown video, but it’s pretty much impossible by now to cover the entire landscape of research. I bet gpt-oss has some great explanations though ;)

Try Andrej Karpathy's YouTube videos. I also really liked the Dive into Deep Learning book at d2l.ai

Start with the YT series on neural nets and LLMs from 3blue1brown

What I’ve sometimes done when trying to make sense of recent LLM research is give the paper and related documents to ChatGPT, Claude, or Gemini and ask them to explain the specific terms I don’t understand. If I don’t understand their explanations or want to know more, I ask follow-ups. Doing this in voice mode works better for me than text chat does.

When I just want a full summary without necessarily understanding all the details, I have an audio overview made on NotebookLM and listen to the podcast while I’m exercising or cleaning. I did that a few days ago with the recent Anthropic paper on persona vectors, and it worked great.

So probably another stupid question, but how do you know what it's spitting out is accurate?

One has to be aware of the possibility of hallucinations, of course. But I have not encountered any hallucinations in these sorts of interactions with the current leading models. Questions like "what does 'embedding space' mean in the abstract of this paper?" yield answers that, in my experience, make sense in the context and check out when compared with other sources. I would be more cautious if I were using smaller models or if I were asking questions about obscure information without supporting context.

Also, most of my questions are not about specific facts but about higher-level concepts. For ML-related topics, at least, the responses check out.

Try Microsoft's "Generative AI for Beginners" repo on GitHub. The early chapters in particular give a good grounding of LLM architecture without too many assumptions of background knowledge. The video version of the series is good too.

Ask Gemini. Give it a link here in fact.

This is a great book (parts of it are available as blog posts from the author if you want to get a taste of it):

https://www.manning.com/books/build-a-large-language-model-f...