For Claude, I can recommend to put / organize relevant source files of your app into the project context/container.

Im also in the opposite boat: Claude is such a boon, it allowed me to boost productivity. Though, I mainly used it always for single functions which Im integrating into the code base. Id say I have a hitquote of 90%+ on the first prompt. Just yesterday I re-prompted a data visualization component which was developed in the first iteration also with Claude (but Sonnet), I had to do 3 simple prompts to get some heavy optimization which wasnt done in the first iteration.

Also I have to say I like its capability to write useful comments a lot.

I’m in the same boat. I’ve been working with a more junior engineer that’s ecstatic about AI coding and I’m slowly settling into the position that, for those of us that have developed tons of opinions about how things should be done, trying to transfer all that experience to an AI through prompting is just not efficient, and I’ve grown comfortable with saying it’s easier for me to do it myself than repeated prompting and adjusting prompts. For a more junior engineer, though, it’s a lot easier to accept what the AI did, and as long as it’s functional, their opinions aren’t strong enough to spark the urge to keep adjusting. Theres just a different utility curve for different people.

Does that mean we’ll get worse (or less opinionated) code over time? Maybe. I used to tell my team that code should be written to be easily understood by maintainers, but if all the maintainers are AI and they don’t care, does it matter?

FWIW, I still reach for Claude once in a while, and I find its response useful maybe one out of ten times, particularly when dealing with code I don’t feel the need to learn or maintain in the long run. But if reviewing Claude’s code requires me to learn the code base properly, often might as well write it myself.

I’m in the opposite boat, having trouble instructing my colleagues on how to get the same success with AI coding that I’ve realized. The issue is that you spend effort “working” the AI to get things done, but at the end of it your only artifact is a bunch of CLI commands executed and…how are you going to describe that?

AI instructions for AI coding really need to be their own code somehow, so programmers can more successfully share their experiences.

I haven't used cursor, so I'm not sure I can be much help there. I've been mostly using claude code and IntelliJ IDEs for code-reviews when necessary. Over the past year I've moved to almost entirely coding via agent. Maybe my input will be helpful.

One very important thing to keep in mind is context management. Every time your agent reads a file, searches documentation, answers a question, writes a file, or otherwise iterates on a problem, the context will grow. The larger the context gets, the dumber the responses. It will basically start forgetting earlier parts of the conversation. To be explicit about this, I've disabled "auto-compact" in claude code and when I see a warning that it's getting too big, I cut things off, maybe ask the agent to commit, or write a summary, and then /compact or /clear. It's important to figure out the context limits of the model you're using and stay comfortably within them.

Next, I generally treat the agent like a mid-level engineer who answers to me. That is to say, I do not try to convince it to code like I do, instead I treat it like a member on my team. When I'm on a team, we stick to standards and use tools like prettier, etc to keep the code in shape. My personal preferences go out the window, unless there's solid technical reason for others to follow them.

With that out of the way, the general loop is to plan with the agent, spec the work to be done, let the agent do the work, review, and repeat. To start, I converse with the agent directly. I'm not writing a spec, I'm discussing the problem with the agent and asking the agent to write the spec. We review, and discuss, and once our decisions are aligned and documented, I'll ask it to break down how it would implement the plan we've agreed upon.

From there I'll keep the context size in mind. If implementation is a multi-hour endeavor, I'll work with the agent to break down the problem into pieces that should ideally fit into the context window. Otherwise, by this point the agent will have asked me "would you like me to go ahead and get started?" and I'll let it get started

Once it's done, I'll ask it to run lint, typechecks, automated testing, do a code review of what's in the current git workspace, compare the changes to the spec, do my own code reviews, run it myself, whatever is needed to make sure what was written solves the problem.

In general, I'd say it's a bad idea to just let the agent go off on its own with a giant task. It should be iterative and communicative. If the task is too big, it WILL take shortcuts. You can probably get an agent to rewrite your whole codebase with a big fancy prompt and a few markdown files. But if you're not part of the process, there's a good chance it'll create a serious mess.

For what you're doing, I would likely like ask the agent to read the mega python file and explain it to me. Then I would discuss what it missed or got wrong and add additional context and explain what needs to be done. Then I would ask it if it has any suggestions for how we should break it into submodules. If the plan looks good, run with it. If not, explain what you're going for and then ask how it would go about extracting the first submodule. If the plan looks good, ask it to write tests, let it extract the submodule, let it run the tests, review the results, do your own code review, tweak the formatting, Goto 10.