For me it was like this for like a year (using Cline + Sonnet & Gemini) until Claude Code came out and until I learned how to keep context real clean. The key breakthrough was treating AI as an architect/implementer rather than a code generator.

Most recently I ask first CC to create a design document for what we are going to do. He has instructions to look into the relevant parts of the code and docs to reference them. I review it and few back-and-forths we have defined what we want to do. Next step is to chunk it into stages and even those to smaller steps. All this may take few hours, but after this is well defined, I clear the context. I then let him read the docs and implement one stage. This goes mostly well and if it doesn't I either try to steer him to correct it, or if it's too bad, I improve the docs and start this stage over. After stage is complete, we commit, clear context and proceed to next stage.

This way I spend maybe a day creating a feature that would take me maybe 2-3. And at the end we have a document, unit tests, storybook pages, and features that gets overlooked like accessibility, aria-things, etc.

At the very end I like another model to make a code review.

Even if this didn't make me faster now, I would consider it future-proofing myself as a software engineer as these tools are improving quickly

This is a common workflow that most advanced users are familiar with.

Yet even following it to a T, and being really careful with how you manage context, the LLM will still hallucinate, generate non-working code, steer you into wrong directions and dead ends, and just waste your time in most scenarios. There's no magical workflow or workaround for avoiding this. These issues are inherent to the technology, and have been since its inception. The tools have certainly gotten more capable, and the ecosystem has matured greatly in the last couple of years, but these issues remain unsolved. The idea that people who experience them are not using the tools correctly is insulting.

I'm not saying that the current generation of this tech isn't useful. I've found it very useful for the same scenarios GP mentioned. But the above issues prevent me from relying on it for anything more sophisticated than that.

For me it's the opposite. As long as I ask for small tasks, or error checking, it can help. But I'd rather think of the overall design myself because I tend to figure out corner cases or superlinear complexities much better. I develop better mental models than the NNs. That's somewhat of a relief.

Also the longer the conversation goes, the less effective it gets. (saturated context window?)

I've seen this referred to as Chain of Thought. I've used it with great success a few times.

https://martinfowler.com/articles/2023-chatgpt-xu-hao.html

Same here. A small variation: I explicitly use website to manage what context it gets to see.