I think this is one of those things where a human in the loop is indispensable
An agentic refactoring pass does make sense cause one LLM reviewing work can spot things the ‘generator’ LLM missed while focused on the initial task output
But can the reviewer agent ever actually have in mind what this project actually is? And how the code all comes together to do the work involved? In other words what parts of the code are redundant or can be made more elegant
Asking coding agents to refactor your codebase is maybe like asking trauma surgeons to increase your exercise capacity. The agents are gonna need a really holistic POV to do this properly
I guess part of my point is that just splitting big files into multiple files is only refactoring in a superficial sense without having a theory of what code belongs together and what can be extracted into utility functions etc. Is splitting files actually like decomposing factors or is it like splitting a larger number into smaller numbers that still eventually get added together
I was able to largely refactor something that was going to take months in a week or so by being the narrating human in the loop.
It was helped by having refactoring experience and approaches to codebases by others, and in my case, being the original architect and being able to speak to the original and current intents, where needed.
This was using a less common, but capable and easy language for the LLM without a ton of dependancy brittleness to manage.. once the effort to remove javascript/python bias was in place, it became so powerful that once the lightbulb went on, it really got cruising.
The project was playing in the world of JSR-223 languages, where you could script in many popular languages, but it all got to run in the JVM, which was an environmental requirement.
I've been budgeting myself explicit "slop removal" time. In fact I'm in it right now one window to my right here.
I still get a big win from AI on the net, but you do need to budget some time to clean up. I'm still on team "read every line".
In fact this is a case where I deliberately deferred some review because I was a blocker for another team. Now that I've got something to them I'm going back and I'm going to eat a bigger chunk of debt than I normally would, but it's worth it for unblocking the other team sooner. AI has made tech debt easier to take out, in all senses of that term.
The benefits go beyond reducing token consumption. Compact contexts also foster better reasoning, enable intelligence across more layers if you can load them in a single context, and writing software to enable this, results in more correct software, software that _generalizes_, that has higher probability of being correct not just for tested cases, but for the interpolations and extrapolations of these cases.
Refactoring towards good abstractions is more powerful than people realize. There's information theoretic bayesian math to back this up.
It's a bit of a divine coincidence that software that is more economically and energy efficient to process and run tends to also be more correct.
the punchline: "Refactoring reduces token consumption"
I appreciate the effort to quantify the benefit rather than pontificate. It's worth mentioning Martin Fowler wrote a whole book on refactoring [1], in which he states, "to refactor, the essential precondition is [...] solid tests", which I think is the real benefit here, AI or not. Good tests protect against regressions, whether human or robot. They also help encode the spec, which humans and robots can read.
Interesting that the amount of code essentially remained unchanged. In my experience it is not unusual that refactoring messy code cuts the number of lines in half.
I don’t know. LOC to me is indispensable for certain refactoring goals in my opinion.
Taking a component and turning it from 7K lines to 3K lines and maintaining functionality obviously means there’s less complexity introduced, less to go wrong now, and less overhead to modify in the future.
Sure it can go the other way, the component needs to support something it might need, we need to adjust larger patterns, this function needs to be refactored into something more robust.
But lines of code is a pretty decent metric of success for “trimming down and cleaning up” style refactoring, to me at least. It’s not everything of course, but it’s definitely an indicator.
LOC can be one measure, but not always the sole or best ones.
Since LLMs are word generators, and have a propensity to generating words, they need to be shaped to understand simplest is best, more isn't more, and less isn't more always.
Trimming down and cleaning up could be formatting, standardization, commenting, or even some basic re-architecting that was overdue.
One of the biggest benefits of llms for refactoring I'm finding is reducing technical debt.
It’s interesting that cyclomatic complexity or cognitive complexity scales with token usage. A codebase that enforces upper bounds on one of these two also (potentially) helps AI agents stay efficient.
Opus/Fable 5 (or really, any thinking model) do a better job of refactoring than Sonnet would. I think the takeaways are still valid, just not as valid when using a more expensive model at a higher effort level.
I have been enforcing this myself through AGENTS files and other explicit instructions. What’s crazy is that none of the existing coding harnesses include such instructions by default. Two lines in Claude Code and Codex and the entire world would be writing better, more efficient code. Makes me believe in the conspiracy that these companies want more verbose code and longer files because it directly results in higher token usage.
Being a bit cheeky here -- the amount of comments on this post is a telltale sign of how common / desirable refactoring is for the majority of developers (xD). It isn't our fault though, maybe if those pesky managers read this article then they would understand..
I think this is one of those things where a human in the loop is indispensable
An agentic refactoring pass does make sense cause one LLM reviewing work can spot things the ‘generator’ LLM missed while focused on the initial task output
But can the reviewer agent ever actually have in mind what this project actually is? And how the code all comes together to do the work involved? In other words what parts of the code are redundant or can be made more elegant
Asking coding agents to refactor your codebase is maybe like asking trauma surgeons to increase your exercise capacity. The agents are gonna need a really holistic POV to do this properly
I guess part of my point is that just splitting big files into multiple files is only refactoring in a superficial sense without having a theory of what code belongs together and what can be extracted into utility functions etc. Is splitting files actually like decomposing factors or is it like splitting a larger number into smaller numbers that still eventually get added together
I was able to largely refactor something that was going to take months in a week or so by being the narrating human in the loop.
It was helped by having refactoring experience and approaches to codebases by others, and in my case, being the original architect and being able to speak to the original and current intents, where needed.
This was using a less common, but capable and easy language for the LLM without a ton of dependancy brittleness to manage.. once the effort to remove javascript/python bias was in place, it became so powerful that once the lightbulb went on, it really got cruising.
The project was playing in the world of JSR-223 languages, where you could script in many popular languages, but it all got to run in the JVM, which was an environmental requirement.
https://en.wikipedia.org/wiki/Scripting_for_the_Java_Platfor...
It's very interesting to have some data on this.
It matches my experience which is that LLMs greatly benefit from well factored code, but are not particularly adept at creating such code.
Much like most human developers I suppose!
I've been budgeting myself explicit "slop removal" time. In fact I'm in it right now one window to my right here.
I still get a big win from AI on the net, but you do need to budget some time to clean up. I'm still on team "read every line".
In fact this is a case where I deliberately deferred some review because I was a blocker for another team. Now that I've got something to them I'm going back and I'm going to eat a bigger chunk of debt than I normally would, but it's worth it for unblocking the other team sooner. AI has made tech debt easier to take out, in all senses of that term.
It is also pretty decent, in my experience, at being guided into how to fix tech debt. Some other people's experience varies: https://news.ycombinator.com/item?id=49035455 YMMV.
Out of the box, this is what I've found too.
I've had some luck while refactoring by helping it shape how to refactor, which can improve how well factored code should look like.
Providing examples of well factored code can go a long way, even if it's an open source repo of what to do / not to do.
The benefits go beyond reducing token consumption. Compact contexts also foster better reasoning, enable intelligence across more layers if you can load them in a single context, and writing software to enable this, results in more correct software, software that _generalizes_, that has higher probability of being correct not just for tested cases, but for the interpolations and extrapolations of these cases.
Refactoring towards good abstractions is more powerful than people realize. There's information theoretic bayesian math to back this up.
It's a bit of a divine coincidence that software that is more economically and energy efficient to process and run tends to also be more correct.
It's all about reducing the entropy of your code. https://benoitessiambre.com/entropy.html
> It's all about reducing the entropy of your code
In all corners of our world and the universe at large, reducing entropy in anything can be thought of as “building.”
the punchline: "Refactoring reduces token consumption"
I appreciate the effort to quantify the benefit rather than pontificate. It's worth mentioning Martin Fowler wrote a whole book on refactoring [1], in which he states, "to refactor, the essential precondition is [...] solid tests", which I think is the real benefit here, AI or not. Good tests protect against regressions, whether human or robot. They also help encode the spec, which humans and robots can read.
[1] https://www.oreilly.com/library/view/refactoring-improving-t...
(Just to note that although the article is on martinfowler.com Martin is not the author. It's attributed to Thoughtworks CTO Giles Edwards-Alexander.)
Thanks for catching that. Updated my post
Interesting that the amount of code essentially remained unchanged. In my experience it is not unusual that refactoring messy code cuts the number of lines in half.
Quality of lines > Quantity of lines.
In some projects, the code might not have much, or enough testing, or documentation/commenting.
Code is largely for others and the future if the creators of it want to move onto other projects.
It can go both ways. Lines of code is a terrible metric for pretty much any goal that refactorings are meant to tackle.
I don’t know. LOC to me is indispensable for certain refactoring goals in my opinion.
Taking a component and turning it from 7K lines to 3K lines and maintaining functionality obviously means there’s less complexity introduced, less to go wrong now, and less overhead to modify in the future.
Sure it can go the other way, the component needs to support something it might need, we need to adjust larger patterns, this function needs to be refactored into something more robust.
But lines of code is a pretty decent metric of success for “trimming down and cleaning up” style refactoring, to me at least. It’s not everything of course, but it’s definitely an indicator.
LOC can be one measure, but not always the sole or best ones.
Since LLMs are word generators, and have a propensity to generating words, they need to be shaped to understand simplest is best, more isn't more, and less isn't more always.
Trimming down and cleaning up could be formatting, standardization, commenting, or even some basic re-architecting that was overdue.
One of the biggest benefits of llms for refactoring I'm finding is reducing technical debt.
It’s interesting that cyclomatic complexity or cognitive complexity scales with token usage. A codebase that enforces upper bounds on one of these two also (potentially) helps AI agents stay efficient.
Opus/Fable 5 (or really, any thinking model) do a better job of refactoring than Sonnet would. I think the takeaways are still valid, just not as valid when using a more expensive model at a higher effort level.
Interesting take-away:
> Claude is unable to look at code, look at refactorings in general and work out which are suitable to apply: a human needs to actively guide it.
Claude is happy to produce a very large Rust file. But you need human guidance to make it smaller.
"I would have written a shorter letter but did not have the time"
Make it work, make it right, make it fast, in that order
Make it right is the refactoring
I have been enforcing this myself through AGENTS files and other explicit instructions. What’s crazy is that none of the existing coding harnesses include such instructions by default. Two lines in Claude Code and Codex and the entire world would be writing better, more efficient code. Makes me believe in the conspiracy that these companies want more verbose code and longer files because it directly results in higher token usage.
Being a bit cheeky here -- the amount of comments on this post is a telltale sign of how common / desirable refactoring is for the majority of developers (xD). It isn't our fault though, maybe if those pesky managers read this article then they would understand..
It's been on HN for 45 minutes. It's before 9am on the west coast