I think you are asking specifically about practical LLM engineering and not the underlying science. Honestly this is all moving so fast you can do well by read

I think you are asking specifically about practical LLM engineering and not the underlying science. Honestly this is all moving so fast you can do well by reading the news, following a few reddits/substacks, and skimming the prompt engineering papers as they come out every week (!). https://www.latent.space/p/ai-engineer https://www.latent.space/p/ai-engineer provides an early manifesto for this nascent layer of the stack. Zvi writes a good roundup (though he is concerned mostly with alignment so skip if you don’t like that angle): https://thezvi.substack.com/p/ai-18-the-great-debate-debates https://thezvi.substack.com/p/ai-18-the-great-debate-debates Simon W has some good writeups too: https://simonwillison.net/ https://simonwillison.net/ I strongly recommend playing with the OpenAI APIs and working with langchain in a Colab notebook to get a feel for how these all fit together. Also, the tools here are incredibly simple and easy to understand (very new) so looking at, say, https://github.com/minimaxir/simpleaichat/tree/main/simpleaichat https://github.com/minimaxir/simpleaichat/tree/main/simpleai... or https://github.com/smol-ai/developer https://github.com/smol-ai/developer and digging in to the prompts, what goes in system vs assistant roles, how you guide the LLM, etc.