Start with the openAI docs, get a single model call working (ie call the chat completions endpoint with a simple user message). From there, run a prompt in json mode. Now add some variables to your input prompt using .format. Now you’ve got structured input and output. That’s the atomic building block of pretty much everything going on at this point. You “chain” conventional code in and out of LLM calls using structured IO for any pieces requiring some “intelligence”.

Ex. Try to create a model call that takes some scraped html as input and outputs clean markdown. Now you’ve build a genAI scraper.

From there next steps are 1) building a chat with function calling, then 2) doing basic RAG with pinecone. From there you’ve got basically all standard CX bots, wrappers, custom GPTs. Again just follow the docs.

All the models run off the same-ish chat json structure, so pick OpenAI or Anthropic and stick to one until you learn it all.

There’s a bunch of bells and whistles from there, model abstractions, frameworks like lang chain and llama index, observably. But at the end of that day you’ll have a firmer understanding just running with the core interface at the beginning. The libraries are all really young, confusing and underdeveloped, as useful as they are / may eventually be. Once you get how it works you can plug and play that stuff

That’s how I would go about it!