But if you don't know how Actix works, how can you be sure that the ChatGPT-generated tutorial is going to be particularly good? It might spit out non-idiomatic, unnecessarily arcane, or even flat-out wrong information, asserted confidently, and you may not have any good way of determining that. Wouldn't you be better off "using the docs yourself" in the first place, assuming they have a halfway decent "Getting Started" section?

I know it's easy to draw an analogy between "AI-assisted coding" and autocompleting IDEs, but under the hood they're really not the same thing. Simpler autocompletion systems offer completions based on the text you've already typed in your project and/or a list of keywords for the current language; smarter ones, like LSP-driven ones, perform some level of introspection of the code. Neither of those pretend to be a replacement for understanding "how the system works." Just because my editor is limiting its autocomplete suggestions to things that make sense at a given cursor position doesn't mean I don't have to learn what those methods actually do. An LLM offering to write a function for you based on, say, the function signature and a docstring does let you skip the whole "learn what the methods actually do" part, and certainly lets you skip things like "what's idiomatic and elegant code for this language". And I think that's what the OP is actually getting at here: you can shortcut yourself out of understanding what you're writing far more easily with an LLM than with even the best non-LLM-based IDE.

Case in point, there is an employee in my office who, after attaining a 4-year CS degree, was unaware that "git" wasn't "GitHub". GitHub is so aggressively pushed on new programmers that they don't even need to know that git exists, much less how it works. Actions that don't have a large colorful button may as well be impossible for someone so thoroughly dependent on tools-in-the-middle.

> Automation bias is the propensity for humans to favor suggestions from automated decision-making systems and to ignore contradictory information made without automation, even if it is correct.

Note this 1998 NASA paper to further refute the IDE/WYSIWYG editor claims.

https://ntrs.nasa.gov/citations/19980048379

> This study clarified that automation bias is something unique to automated decision making contexts, and is not the result of a general tendency toward complacency.

The problems with automation bias has been known for decades and the studies in the human factors field is quite robust.

While we are still way to early in the code assistant world to have much data IMHO, there is evidence called out even in studies that edge towards positive results in coding assistants that point out issues with complacency and automation bias.

https://arxiv.org/abs/2208.14613

> On the other hand, our eye tracking results of RQ2 suggest that programmers make fewer fixations and spend less time reading code during the Copilot trial. This might be an indicator of less inspection or over-reliance on AI (automation bias), as we have observed some participants accept Copilot suggestions with little to no inspection. This has been reported by another paper that studied Copilot [24].