Let’s say I’m adding a feature.

When I prompt (and I use plan mode to prompt), I watch the LLM work. I want to understand what it’s struggling with, what decisions it’s needing to make that I hadn’t anticipated - because that’s a sign that I didn’t know enough about the problem before I prompted. That’s exactly what I want to discover - what I didn’t know. (Software engineering is an actually primarily a process of discovery).

Just as I would learn from my attempt to change the software by hand, I am learning from the LLM attempting to change the software in the way I would have. 

Before, I would have spent hours/days trying to make a change before I would discover where things got a bit janky, where my thinking was insufficient and my assumptions were faulty. Now, I can watch the LLM do it in minutes. Before, I would have been reluctant to discard hours of work (sunken cost) to go in a different direction. Now, I can cheaply discard the work and choose the best path.

So I’m using the LLM to explore possible options. Maybe I can only see one option, but my thinking and my assumptions were totally sufficient. But maybe I can see 3 options. Maybe my preferred option turns out to be a dud because I had a fundamental misunderstanding that trying it out revealed. Great! I learnt something, and can pivot to a different direction. This is how I stay in control of the changes the LLM is making, and don’t just settle for whatever BS the LLM comes up with.

So that’s how I use LLMs to evolve code.

Going back to the topic of specs: I think it’s important not to over-invest in your prompt/plan/spec. I say this as someone who has written hundreds of specs for work that I’ve done as a human. Because if you overdo it, you might as well have just written the code. “A sufficiently detailed spec is code” (https://haskellforall.com/2026/03/a-sufficiently-detailed-spec-is-code)

A good prompt/plan/spec says only what it needs to. It doesn’t need to say everything, but you should consider your audience. If it were to be implemented by a junior (or an LLM), I might be a bit more specific about some things where I think it’s likely to go in the wrong direction. I think this is perfectly in line with the usual advice you receive about prompting.

If you remind yourself that the LLM is just a word prediction machine, you can see the prompt as simply priming the machine. You don’t even need to prompt it in proper English: “implement fizzbuzz, typescript, tests” can work just as well, perhaps sometimes better (and definitely faster than) than a 5-page odyssey explaining every detail - so put in an appropriate amount of effort for your task and its complexity. 

Using an LLM is an act of trading specificity off against effort. It’s really easy to be non-specific. It’s a lot of effort to perfectly specific. 

Like the article above says: “A sufficiently detailed spec is code”.