Many developers don't realize this but as you go back and forth with models, you are actively polluting their context with junk and irrelevant old data that distracts and confuses it from what you're actually trying to do right now. When using sleeker products like Cursor, it's easy to forget just how much junk context the model is constantly getting fed (from implicit RAG/context gathering and hidden intermediate steps). In my experience LLM performance falls off a cliff somewhere around 4 decent-sized messages, even without including superfluous context.
We're further separating the concept of "workflow" from "conversation" and prompts, basically actively and aggressively pruning context and conversation history as our agents do their thing (and only including context that is defined explicitly and transparently), and it's allowing us to tackle much more complex tasks than most other AI developer tools. And we are a lot happier working with models - when things don't work we're not forced to grovel for a followup fix, we simply launch a new action to make the targeted change we want with a couple clicks.
It is in a weird way kind of degrading to have to politely ask a model to change a color after it messed up, and it's also just not an efficient way to work with LLMs - people just default to that style because it's how you'd interact with a human you are delegating tasks to. Developers still need to truly internalize the facts that LLMs are purely completion machines, that your conversation history lives entirely client side outside of active inference, and that you can literally set your conversation input to be whatever you want (even if the model never said that) - after that realizing you're on the path towards using LLMs like "what words do I need to put it in to get it to do what I want" rather than working "with" them.