While that may be true, you can be confident that it works in a way you personally understand. Because you understood it in the past.
Also, if you finish your work on a module with care - you return to a module you can trust with clear boundraries and known flaws. This is not true of AI output.
I'm skeptical of this. I have yet to build a codebase using AI that I wasn't able to subsequently understand after taking the time to do so, esp. when aided by AI that will tirelessly answer my questions. I mean, LLMs don't always code the exact way I do, but it's not writing totally alien code that's incomprehensible to the human mind here.
I use Claude Code, primarily writing web applications and JavaScript. I'll have it use various frameworks. It's built apps for me using React, Ember, Astro, etc. I don't restrict myself to frameworks I'm particularly familiar with, since it's easy for the AI to teach me the basics whenever I would like to dig in.
As for specific practices, these are my main ones:
- I have a gotchas-log.md file that acts as a log of gotchas likely to trip up future runs. I have the AI write to this occasionally when things go haywire in the same way multiple times. And I have it read it as part of its iterative reviews, described below.
- I have a good-code-guidelines.md file where I write my preferences for code. I have the AI read this as part of iterative reviews, described below.
- I have a plan-and-execute.md file that prompts the LLM to make a plan, and then to review and iterate on that plan repeatedly (while reading gotchas-log.md and
good-code-guidelines.md) until its reviews stop finding issues. I tag this file to implement almost every non-trivial change.
- I have other various helper prompts. For example, I can simply tag @make-a-git-commit.md and it tells the LLM to make a commit and write the message the way I like it. I have @simplify.md, which I can tag to have the LLM explain whatever it just did to me using simple language that makes it easier for me to understand, and using concentric circles of explanation that go from broad to specific so I'll repeatedly encounter important topics; this makes it much more bearable for me to read its responses.
- Occasionally, whenever a particular system of my codebase starts to get hairy, I spawn a Claude Code session to read through and trace all the relevant code paths, then write a short guide to that system in a markdown file that lives in the codebase. IT's useful for me to read and also useful to tag for future prompts to get the LLM up to speed quickly. Only challenge here is that these guides go stale and require updating, so it's important to prompt the AI to write them at the appropriate level (not to specific) that prevents them from being overly brittle and getting out of date with every little change. They're mostly high-level guides.
No. You can absolutely build things with AI small or large and understand it. If you don’t understand it. Then you’re not caring about the output to begin with and not guiding it to build the solution you want.
The word "understand" seems to mean something different to you.
If I understand something, I could write it in assembly if I wanted to. It might take a long time, but I know every level of the stack under my code down to bare metal.
Maybe an AI level of "understand" i.e the same understanding a Senior has of a Junior's code based on daily check-ins is enough for 95% of "boring" programming. But for some tasks you need to either fully understand the code or just tolerate bugs.
At the level of complexity I work at, it's (often) faster to just code it myself than to expect AI to converge on a result I like and then hand check it.
Look, goals can differ. If you don't need to understand and predict every part of the code the AI is generating and you just need it to meet a "sketch" of what you want - by all means use AI. I do use AI in that situation for related or unimportant code.
But if you need full understanding, in my experience the only way to get that is to program it yourself. Unless what the AI is generating is so trivial you already understand it and it's grunt work, you will learn the detail by doing it yourself. Controlling the approach here is important.
However often the best way to handle grunt work is to write better abstractions, something AI sucks at.
It's the programming equivalent of many PG essays. https://www.paulgraham.com/useful.html for instance. There was one on how PG refined their thoughts via writing. It's in there somewhere.
> At the level of complexity I work at, it's (often) faster to just code it myself than to expect AI to converge on a result I like and then hand check it.
Would love an example because no one has ever been able to give a coding example that AI isn’t helpful for. I had one person on linked in try claim their undocumented audio hardware won’t work with ai but when we got ai to probe it and build docs it ended up solving a bunch of complex bugs they couldn’t fix.
Impressive, also horrifying. I love what I do and if AI can do it better than that sucks.
Anyway the use case for me is to realize a new visual style through graphics programming. It's a lot less measurable for an interative AI agent than "Meet this hardware specification from a device with a discoverable API"
I have no doubt AI could create a LOT of variations on "a new visual style" but it's less controllable than just doing it yourself.
Btw, did you "understand" the sound driver after the AI coded it? Could you modify it without further help?
Also, if you finish your work on a module with care - you return to a module you can trust with clear boundraries and known flaws. This is not true of AI output.