Docs bots like these are deceptively hard to get right in production. Retrieval is super sensitive to how you chunk/parse documentation and how you end up structuring documentation in the first place (see frontpage post from a few weeks ago: https://news.ycombinator.com/item?id=44311217).
You want grounded RAG systems like Shopify's here to rely strongly on the underlying documents, but also still sprinkle a bit of the magic of the latent LLM knowledge too. The only way to get that balance right is evals. Lots of them. It gets even harder when you are dealing with GraphQL schema like Shopify has since most models struggle with that syntax moreso than REST APIs.
FYI I'm biased: Founder of kapa.ai here (we build docs AI assistants for +200 companies incl. Sentry, Grafana, Docker, the largest Apache projects etc).
We concatenated all our docs and tutorials into a text file, piped it all into the AI right along with the question, and the answers are pretty great. Cost was, last I checked, roughly 50c per question. Probably scales linearly with how much docs you have. This feels expensive but compared to a human writing an answer it's peanuts. Plus (assuming the customer can choose to use the AI or a human), it's great customer experience because the answer is there that much faster.
I feel like this is a no-brainer. Tbh with the context windows we have these days, I don't completely understand why RAG is a thing anymore for support tools.