11 ms·

Scaling LLMs to Larger Codebases

307 points by kierangill 9mo ago

uoaei 9mo ago
What is the current state of LCMs (large code models)? I.e. models that operate on the AST and not on text tokens.
rootnod3 9mo ago
Or why you shouldn't....
CuriouslyC 9mo ago
STAN'd to the top. Decent article but it feels like a linkedin rehashing of stuff the people at the edge have already known for a while.
- Aurornis 9mo ago
  > but it feels like a linkedin rehashing of stuff the people at the edge have already known for a while. You're not wrong, but it bears repeating to newcomers. The average LLM user I encounter is still just hammering questions into the prompt and getting frustrated when the LLM makes the same mistakes over and over again.
Aurornis 9mo ago
> Making a prompt library useful requires iteration. Every time the LLM is slightly off target, ask yourself, "What could've been clarified?" Then, add that answer back into the prompt library. I'm far from an LLM power user, but this is the single highest ROI practice I've been using. You have to actually observe what the LLM is trying to do each time. Simply smashing enter over and over again or setting it to auto-accept everything will just burn tokens. Instead, see where it gets stuck and add a short note to CLAUDE.md or equivalent. Break it out into sub-files to open for different types of work if the context file gets large. Letting the LLM churn and experiment for every single task will make your token quota evaporate before your eyes. Updating the context file constantly is some extra work for you, but it pays off. My primary use case for LLMs is exploring code bases and giving me summaries of which files to open, tracing execution paths through functions, and handing me the info I need. It also helps a lot to add some instructions for how to deliver useful results for specific types of questions.
- CPLX 9mo ago
  I'm with you on that, but I have to say I have been doing that aggressively, and it's pretty easy for Claude Code at least to ignore the prompts, commands, Markdown files, README, architecture docs, etc. I feel like I spend quite a bit of time telling the thing to look at information it already knows. And I'm talking about when I HAVE actually created various documents to use and prompts. As a specific example, it regularly just doesn't reference CLAUDE.md and it seems pretty random as to when it decides to drop that out of context. That's including right at session start when it should have it fresh.
  - Aurornis 9mo ago
    > and it's pretty easy for Claude Code at least to ignore the prompts, commands, Markdown files, README, architecture docs, etc. I would agree with that! I've been experimenting with having Claude re-write those documents itself. It can take simple directives and turn them into hierarchical Markdown lists that have multiple bullet points. It's annoying and overly verbose for humans to read, but the repetition and structure seems to help the LLM. I also interrupt it and tell it to refer back to CLAUDE.md if it gets too off track. Like I said, though, I'm not really an LLM power user. I'd be interested to hear tips from others with more time on these tools.
  - zarp 9mo ago
    > it seems pretty random as to when it decides to drop that out of context Overcoming this kind of nondeterministic behavior around creating/following/modifying instructions is the biggest thing I wish I could solve with my LLM workflows. It seems like you might be able to do this through a system of Claude Code hooks, but I've struggled with finding a good UX for maintaining a growing and ever-changing collection of hooks. Are there any tools or harnesses that attempt to address this and allow you to "force" inject dynamic rules as context?
    - lkjdsklf 9mo ago
      Wouldn't it be great if we had some kind of deterministic language to precisely and concisely tell a computer what to do
mym1990 9mo ago
Its kind of crazy that the knee jerk reaction to failing to one shot your prompt is to abandon the whole thing because you think the tool sucks. It very well might, but it could also be user error or a number of other things. There wouldn't be a good nights sleep in sight if I knew an LLM was running rampant all over production code in an effort to "scale it".
- t_tsonev 9mo ago
  I'm okay with writing developer docs in the form of agent instructions, those are useful for humans too. If they start to get oddly specific or sound mental, then it's obviously the tool at fault.
- zeroonetwothree 9mo ago
  There’s always a trade off in terms of alternative approaches. So I don’t think it’s “crazy” that if one fails you switch to a different one. Sure, sometimes persistence can pay off, but not always. Like if I go to a restaurant for the first time and the item I order is bad, could I go back and try something else? Perhaps, but I could also go somewhere else.
smallerize 9mo ago
This highlights a missing feature of LLM tooling, which is asking questions of the user. I've been experimenting with Gemini in VS Code, and it just fills in missing information by guessing and then runs off writing paragraphs of design and a bunch of code changes that could have been avoided by asking for clarification at the beginning.
- skolos 9mo ago
  Claude code regularly asks me questions - I like how anthropic implemented this
  - rockbruno 9mo ago
    Yeah I experienced this yesterday and it was really cool. It really only happened once though.
  - hobofan 9mo ago
    So does Cursor in the Plan mode.
- zvorygin 9mo ago
  Append “First ask clarifying questions” to your prompt.
- pteetor 9mo ago
  For complicated prompts, I always add this: "Before you start, please ask me any questions you have about this so I can give you more context. Be extremely comprehensive." (I got the idea from a Medium article[1].) The LLM will, indeed, stop and ask good questions. It often notices what I've overlooked. Works very well for me! [1] https://medium.com/@jordan_gibbs/the-most-important-chatgpt-prompt-55ef2bdc4d4a https://medium.com/@jordan_gibbs/the-most-important-chatgpt-...
- tharkun__ 9mo ago
  So like most junior to mid level devs ;) Claude does have this specific interface for asking questions now. I've only had it choose to ask me questions on its own a very few times though. But I did have it ask clarifying questions before that interface was even a thing, when I specifically asked it to ask me clarifying questions. Again, like a junior dev. And like a junior dev, it can also help to ask it to ask / check what its doing "mid-way", i.e. watch what it's doing and stop it, when it's running down some rabbit hole you know is not gonna yield results.
andrewmutz 9mo ago
The issues raised in this article are why I think highly-opinionated frameworks will lead to higher developer productivity when using AI assisted coding You may not like all the opinions of the framework, but the LLM knows them and you don’t need to write up any guidelines for it.
- christophilus 9mo ago
  Yep. I ran an experiment this morning building the same app in Go, Rust, Bun, Ruby (Rails), Elixir (Phoenix), and C# (ASP whatever). Rails was a done deal almost right away. Bun took a lot of guidance, but I liked the result. The rest was a lot more work with so-so results — even Phoenix, surprisingly. I liked the Rust solution a lot, but it had 200+ dependencies vs Bun’s 5 and Rails’ 20ish (iirc). Rust feels like it inherited the NPM “pull in a thousand dependencies per problem” philosophy, which is a real shame.
- some-guy 9mo ago
  I can vouch for this as someone who works in a 1.6 million line codebase, where there are constant deviations and inconsistent patterns. LLMs have been almost completely useless on it other than for small functions or files.
vivin 9mo ago
You can't get away from the engineering part of software engineering even if you are using LLMs. I have been using Claude Opus 4.5, and it's the best out of the models I have tried. I find that I can get Claude to work well if I already know the steps I need to do beforehand, and I can get it to do all of the boring stuff. So it's a series of very focused and directed one-shot prompts that it largely gets correct, because I'm not giving it a huge task, or something open-ended. Knowing how you would implement the solution beforehand is a huge help, because then you can just tell the LLM to do the boring/tedious bits.
- teaearlgraycold 9mo ago
  They’re good for getting you from A to B. But you need to know A (current state of the code) and how to get to B (desired end state). They’re fast typers not automated engineers.
- ericmcer 9mo ago
  seriously, I stopped agent mode altogether. I hit it with very specific like: write a function that takes an array of X and returns y. It almost never fails and usually does it in a neat way, plus its ~50 lines of code so I can copy and paste confidently. Letting the agent just go wild on my code has always been a PITA for me.
  - vivin 9mo ago
    I've used agent mode, but I tell it not to go hog wild and to not do anything other than what I have instructed it to do. Also, sometimes I will tell it not to change the code, and to go over its changes with me first, before I tell it that it can make the changes. I feel the same way as you in general -- I don't trust it to go and just make changes all over the codebase. I've seen it do some really dumb stuff before because it doesn't really understand the context properly.
tschellenbach 9mo ago
I wrote this forever ago in AI terms :) https://getstream.io/blog/cursor-ai-large-projects/ https://getstream.io/blog/cursor-ai-large-projects/ But the summary here is that with the right guidance, AI currently crushes it on large codebases.
- deleted 9mo ago
  [deleted]
mstank 9mo ago
As the models have progressively improved (able to handle more complex code bases, longer files, etc) I’ve started using this simple framework on repeat which seems to work pretty well at one shorting complex fixes or new features. [Research] ask the agent to explain current functionality as a way to load the right files into context. [Plan] ask the agent to brainstorm the best practices way to implement a new feature or refactor. Brainstorm seems to be a keyword that triggers a better questioning loop for the agent. Ask it to write a detailed implementation plan to an md file. [clear] completely clear the context of the agent —- better results than just compacting the conversation. [execute plan] ask the agent to review the specific plan again, sometimes it will ask additional questions which repeats the planning phase again. This loads only the plan into context and then have it implement the plan. [review & test] clear the context again and ask it to review the plan to make sure everything was implemented. This is where I add any unit or integration tests if needed. Also run test suites, type checks, lint, etc. With this loop I’ve often had it run for 20-30 minutes straight and end up with usable results. It’s become a game of context management and creating a solid testing feedback loop instead of trying to purely one-shot issues.
- AlexB138 9mo ago
  This is essentially my exact workflow. I also keep the plan markdown files around in the repo to refer agents back to when adding new features. I have found it to be a really effective loop, and a great way to reprime context when returning to features.
  - mstank 9mo ago
    Exactly this. I clear the old plans every few weeks. For really big features or plans I’ll ask the agent to create linear issue tickets to track progress for each phase over multiple sessions. Only MCP I have loaded is usually linear but looking for a good way to transition it to a skill.
    - AlexB138 9mo ago
      Ah, that's a great idea. I've just been having the agent add a Progress section to the plan files and checking things off as we work.
pron 9mo ago
> Here's a LLM literacy dipstick: ask a peer engineer to read some code they're unfamiliar with. Do they understand it? ... No? Then the LLM won't either. Of course, but the problem is the converse: There are too many situations where a peer engineer will know what to do but the agent won't. This means that it requires more work to make a codebase understandable to a human than it does to make it understandable to an agent. > Moving more implementation feedback from human to computer helps us improve the chance of one-shotting... Think of these as bumper rails. You can increase the likelihood of an LLM reaching the bowling pins by making it impossible to land in the gutter. Sort of, but this is also a little similar to claiming that P = NP. Having a an efficient way to reliably check if a solution is correct is not the same at all as a reliable way to find a solution. It's the theory of computation that tells us that it probably isn't. The likelihood may well be higher yet still not high enough. Even though theoretically NP problems are strictly easier than EXPTIME ones, in practice, in many situations (though not all) they are equally intractable. In fact, we can put the claim to the test: there are languages, like ATS and Idris, that make almost any property provable and checkable. These languages let the programmer (human or machine) position the "bumper rails" so precisely as to ensure we hit the target. We can ask the agent to write the code, write the proof of correctness, and check it. We'd still need to check that the correctness property is the right one, but if the claim is correct, coding agents should be best at writing code, accompanied by correctness proofs, in ATS or Idris. Are they? Obviously, mileage mauy vary dependning on the task and the domain, but if it's true that coding models will get significantly better, then the best course of action may well be, in many cases, to just wait until they do rather than spend a lot of effort working around their current limitations, effort that will be wasted if and when capabilities improve. And that's the big question: are we in for a long haul where agent capabilities remain roughly where they are today or not?
victorbjorklund 9mo ago
Biggest change to my workflow has been to break down projects to smaller parts using libraries. So where I in the past would put everything in the same code base I now break down stuff that can be separate to its own libraries (like wrapping an external API). That way the AI only needs to read the docs for the library instead of having to read all the code when working on features that use the API.
EastLondonCoder 9mo ago
I’ve ended up with a workflow that lines up pretty closely with the guidance/oversight framing in the article, but with one extra separation that’s been critical for me. I’m working on a fairly messy ingestion pipeline (Instagram exports → thumbnails → grouped “posts” → frontend rendering). The data is inconsistent, partially undocumented, and correctness is only visible once you actually look at the rendered output. That makes it a bad fit for naïve one-shotting. What’s worked is splitting responsibility very explicitly: • Human (me): judge correctness against reality. I look at the data, the UI, and say things like “these six media files must collapse into one post”, “stories should not appear in this mode”, “timestamps are wrong”. This part is non-negotiably human. • LLM as planner/architect: translate those judgments into invariants and constraints (“group by export container, never flatten before grouping”, “IG mode must only consider media/posts/*”, “fallback must never yield empty output”). This model is reasoning about structure, not typing code. • LLM as implementor (Codex-style): receives a very boring, very explicit prompt derived from the plan. Exact files, exact functions, no interpretation, no design freedom. Its job is mechanical execution. Crucially, I don’t ask the same model to both decide what should change and how to change it. When I do, rework explodes, especially in pipelines where the ground truth lives outside the code (real data + rendered output). This also mirrors something the article hints at but doesn’t fully spell out: the codebase isn’t just context, it’s a contract. Once the planner layer encodes the rules, the implementor can one-shot surprisingly large changes because it’s no longer guessing intent. The challenges are mostly around discipline: • You have to resist letting the implementor improvise. • You have to keep plans small and concrete. • You still need guardrails (build-time checks, sanity logs) because mistakes are silent otherwise. But when it works, it scales much better than long conversational prompts. It feels less like “pair programming with an AI” and more like supervising a very fast, very literal junior engineer who never gets tired, which, in practice, is exactly what these tools are good at.
tracker1 9mo ago
Just over the weekend, I decided to shell out for the top tier Claude Code to give it a try... definitely an improvement over the year I spent with Github CoPilot enabled on my personal projects (mostly an annoyance more than a help that I eventually disabled altogether). I've seen some impressive output so far, and have a couple friends that have been using AI generation a lot... I'm trying to create a couple legacy (BBS tech related, in Rust) applications to see how they land. So far mostly planning and structure beyond the time I've spent in contemplation. I'm not sure I can justify the expense long term, but wanting to experience the fuss a bit more to have at least a better awareness.
dmofp 9mo ago
I have a somewhat different take on this (somewhat captured in the post linked below). IMO, the best way to raise the floor of LLM performance in codebases is by building meaning into the code base itself ala DDD. If your codebase is hard to understand and grok for a human, it will be the same for an LLM. If your codebase is unstructured and has no definable patterns, it will be harder for an LLM to use. You can try to overcome this with even more tooling and more workflows but IMO, it is throwing good money after bad. it is ironic and maybe unpopular, but it turns out LLMs prove that all the folks yapping about language and meaning (re: DDD) were right. DDD & the Simplicity Gospel: https://oluatte.com/posts/domain-driven-design-simplicity-gospel https://oluatte.com/posts/domain-driven-design-simplicity-go...
- dj_gitmo 9mo ago
  Great post. I work on two large codebases. One is structured much like the example from the post, and the other is a mess. LLMs care much better at understanding the organized code.
__MatrixMan__ 9mo ago
I'm interested to see where we'll land re: organizing larger codebases to accommodate agents. I've been having a lot of fun taking my larger projects and decomposing them into directed graphs where the nodes are nix flakes. If I launch claude code in a flake devshell it has access to only those tools, and it sees the flake.nix and assumes that the project is bounded by the CWD even though it's actually much larger, so its context is small and it doesn't get overwhelmed. Inputs/outputs are a nice language agnostic mechanism for coordinating between flakes (just gotta remember to `nix flake update --update-input` when you want updated outputs from an adjacent flake). Then I can have them write feature requests for each other and help each other test fixtures and features. I also like watching them debate over a design, they get lazy and assume the other "team" will do the work, but eventually settle on something reasonable. I've been running with the idea for a few weeks, maybe it's dumb, but I'd be surprised if this kind of rethinking didn't eventually yield a radical shift in how we organize code, even if the details look nothing like what I've come up with. Somehow we gotta get good at partitioning context so we can avoid the worst parts of the exponential increase in token volume that comes from submitting the entire chat session history just to get the next response.
- quinnjh 9mo ago
  yeah this is an interesting approach, both for the context-partitioning but also for reproducibility and dependency pinning. i was toying with this before needing to run with just docker on a project. would be nice to find a tool that streamlines some of this
  - __MatrixMan__ 9mo ago
    Re: dependency pinning, I put together a little write-up about that: https://gist.github.com/MatrixManAtYrService/6eaf50373448c0bc14acca31d69591b9 https://gist.github.com/MatrixManAtYrService/6eaf50373448c0b... You can use it as an alternative to `git bisect` where only you're only bisecting the history of a single subflake. I imagine writing a new test that indicates the presence of an old bug, and then going back in time to see when the bug was reintroduced. With git bisect, going back in time means your new test goes away too.
lnx01 9mo ago
LLMs are so good at telling me about things I know little to nothing about, but when when I ask about things I have expert knowledge on they consistently fail, hallucinate, and confidently lie...
- dmoy 9mo ago
  Feels like https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect
  - marcosdumay 9mo ago
    It was a very clear sarcastic reference to it. But it's still not completely right. LLMs are actually great to tell you about things you know little about. You just have to take names, ideas, and references from it, not facts. (And that makes agentic coding almost useless, by the way.)
- llmslave2 9mo ago
  I think you end up asking it basic questions about stuff you know little about, but much more complex/difficult questions for stuff you're already an expert in.
- christophilus 9mo ago
  I’ve found that they vary a huge amount based on the subject matter. In my case, I have noticed the opposite of what you observed. They know a lot about the web space (which I’ve been in for around 25 years), but are pretty bad (though not useless) at esoteric languages such as Hare.
  - drcxd 9mo ago
    Obviously, since the training material for such esoteric languages is scarce. (That's why they are esoteric!) So by definition, LLM will never be good at esoteric languages.
- throw-12-16 9mo ago
blauditore 9mo ago
It's like people are rediscovering the most basic principles: E.g. that documentation ("prompt library") is usecho, or that well-organized code leads to higher velocity in development.
- hu3 9mo ago
  if that's what it takes for more people to write tests, then so be it
laser9 9mo ago
> This is the garbage in, garbage out principle in action. The utility of a model is bottlenecked by its inputs. The more garbage you have, the more likely hallucinations will occur. Good read but I wouldn't fully extend the garbage in, garbage out principle to the LLMs. These massive LLMs are trained on internet-scale data, which includes a significant amount of garbage, and still do pretty good. Hallucinations are due to missing or misleading context than from the noise alone. Tech debt heavy code bases though unstructured still provides information-rich context.
patcon 9mo ago
Is it not the case that "production level code" coming out of these processes makes the whole system of coder-plus-machine weaker? I find it to be a good thing that the code must be read in order to be production-grade, because that implies the coder must keep learning. I worry about the collapse in knowledge pipeline when there is very little benefit to overseeing the process... I say that as a bad coder who can and has done SO MUCH MORE with llm agents. So I'm not writing this as someone who has an ideal of coding that is being eroded. I'm just entering the realm of "what elite coding can do" with LLMs, but I worry for what the realm will lose, even as I'm just arriving
spullara 9mo ago
Using AugmentCode's Context Engine you can get this either through their VSCode/JetBrains plugins, their Auggie command line coding agent or by registering their MCP server with your local coding agent like Claude Code. It works far better than painstakingly stuffing your own context manually or having your agent use grep/lsp/etc to try and find what it needs.
avree 9mo ago
Why do none of these ever touch on token optimization? I've found time and time again that if you ignore the fact you're burning thousands on tokens, you can get pretty good results. Things like prompt libraries and context.md files tend to just burn more tokens per call.
hobofan 9mo ago
> Aside: Why are LLMs good at greenfield? I have the complete opposite experience, where once some patterns already exist 2-3 times in the codebase, the LLMs start to accurately replicating them instead of trying to solve everything as one-off solutions. > You can’t be inconsistent if there are no existing patterns. "Consistency" shouldn't be equated to "good". If that's your only metric for quality and you don't apply any taste you'll quickly end of with a unmaintainable hodgepodge of second-grade libraries if you let an LLM do its thing in a greenfield project.
throw-12-16 9mo ago
There is no way this is economical. Burn through your token limit in agent mode just to thrash around a few more times trying to identify where the agent "misunderstood" the prompt. The only time LLM's work as coding agents for me is tightly scoped prompts with a small isolated context. Just throwing an entire codebase into an LLM in an agentic loop seems like a fools errand.
eurekin 9mo ago
If you're interested in the large codebase... The best I found so far are extended context models. Using newest Nemotron3 nano, you can put a 1m tokens (about 3 ish megabytes of text) of pure code dump (I use repomix --style markdown) and ask around. That's been one of the biggest wow moments I had with LLMs so far. Much better experience than any RAG I used
Simplita 9mo ago
One thing that helped us as codebases grew was separating decision-making from execution. Let the model reason about intent and scope, but keep execution deterministic and constrained. It reduced drift and made failures much easier to debug once context got large.
ColinEberhardt 9mo ago
“When an LLM can generate a working high-quality implementation in a single try, that is called one-shotting. This is the most efficient form of LLM programming.” This is a good article, but misses one of the most important advances this year - the agentic loop. There are always going to be limits to how much code a model can one-shot. Give it the ability to verify its changes and iterate, massively increase its ability to write sizeable chunks of verified and working code.
jukkat 9mo ago
Put every detail into CLAUDE.md and after a while CC starts to forget/ignore what it’s been told. I’d like to see dynamic task-specific context building. Write a prompt and the model starts to collect relevant instructions. Also a review loop to check that instructions were followed.
Ayanonymous 9mo ago
I'm still learning about how LLMs can be used in coding, but this article helped me understand the importance of giving clear instructions and not relying too much on automation. The point about developers still needing to guide the model really makes sense. Thanks for sharing this!
eddywebs 9mo ago
How about adding MCP support to large code bases to provide RAG based context to LLMs, ive been playing with this idea with some good results.

307 points by kierangill 9mo ago

uoaei 9mo ago

What is the current state of LCMs (large code models)? I.e. models that operate on the AST and not on text tokens.

rootnod3 9mo ago

Or why you shouldn't....

CuriouslyC 9mo ago

STAN'd to the top. Decent article but it feels like a linkedin rehashing of stuff the people at the edge have already known for a while.

Aurornis 9mo ago
> but it feels like a linkedin rehashing of stuff the people at the edge have already known for a while. You're not wrong, but it bears repeating to newcomers. The average LLM user I encounter is still just hammering questions into the prompt and getting frustrated when the LLM makes the same mistakes over and over again.

Aurornis 9mo ago

> Making a prompt library useful requires iteration. Every time the LLM is slightly off target, ask yourself, "What could've been clarified?" Then, add that answer back into the prompt library. I'm far from an LLM power user, but this is the single highest ROI practice I've been using. You have to actually observe what the LLM is trying to do each time. Simply smashing enter over and over again or setting it to auto-accept everything will just burn tokens. Instead, see where it gets stuck and add a short note to CLAUDE.md or equivalent. Break it out into sub-files to open for different types of work if the context file gets large. Letting the LLM churn and experiment for every single task will make your token quota evaporate before your eyes. Updating the context file constantly is some extra work for you, but it pays off. My primary use case for LLMs is exploring code bases and giving me summaries of which files to open, tracing execution paths through functions, and handing me the info I need. It also helps a lot to add some instructions for how to deliver useful results for specific types of questions.

CPLX 9mo ago
I'm with you on that, but I have to say I have been doing that aggressively, and it's pretty easy for Claude Code at least to ignore the prompts, commands, Markdown files, README, architecture docs, etc. I feel like I spend quite a bit of time telling the thing to look at information it already knows. And I'm talking about when I HAVE actually created various documents to use and prompts. As a specific example, it regularly just doesn't reference CLAUDE.md and it seems pretty random as to when it decides to drop that out of context. That's including right at session start when it should have it fresh.
- Aurornis 9mo ago
  > and it's pretty easy for Claude Code at least to ignore the prompts, commands, Markdown files, README, architecture docs, etc. I would agree with that! I've been experimenting with having Claude re-write those documents itself. It can take simple directives and turn them into hierarchical Markdown lists that have multiple bullet points. It's annoying and overly verbose for humans to read, but the repetition and structure seems to help the LLM. I also interrupt it and tell it to refer back to CLAUDE.md if it gets too off track. Like I said, though, I'm not really an LLM power user. I'd be interested to hear tips from others with more time on these tools.
- zarp 9mo ago
  > it seems pretty random as to when it decides to drop that out of context Overcoming this kind of nondeterministic behavior around creating/following/modifying instructions is the biggest thing I wish I could solve with my LLM workflows. It seems like you might be able to do this through a system of Claude Code hooks, but I've struggled with finding a good UX for maintaining a growing and ever-changing collection of hooks. Are there any tools or harnesses that attempt to address this and allow you to "force" inject dynamic rules as context?
  - lkjdsklf 9mo ago
    Wouldn't it be great if we had some kind of deterministic language to precisely and concisely tell a computer what to do

mym1990 9mo ago

Its kind of crazy that the knee jerk reaction to failing to one shot your prompt is to abandon the whole thing because you think the tool sucks. It very well might, but it could also be user error or a number of other things. There wouldn't be a good nights sleep in sight if I knew an LLM was running rampant all over production code in an effort to "scale it".

t_tsonev 9mo ago
I'm okay with writing developer docs in the form of agent instructions, those are useful for humans too. If they start to get oddly specific or sound mental, then it's obviously the tool at fault.
zeroonetwothree 9mo ago
There’s always a trade off in terms of alternative approaches. So I don’t think it’s “crazy” that if one fails you switch to a different one. Sure, sometimes persistence can pay off, but not always. Like if I go to a restaurant for the first time and the item I order is bad, could I go back and try something else? Perhaps, but I could also go somewhere else.

smallerize 9mo ago

This highlights a missing feature of LLM tooling, which is asking questions of the user. I've been experimenting with Gemini in VS Code, and it just fills in missing information by guessing and then runs off writing paragraphs of design and a bunch of code changes that could have been avoided by asking for clarification at the beginning.

skolos 9mo ago
Claude code regularly asks me questions - I like how anthropic implemented this
- rockbruno 9mo ago
  Yeah I experienced this yesterday and it was really cool. It really only happened once though.
- hobofan 9mo ago
  So does Cursor in the Plan mode.
zvorygin 9mo ago
Append “First ask clarifying questions” to your prompt.
pteetor 9mo ago
For complicated prompts, I always add this: "Before you start, please ask me any questions you have about this so I can give you more context. Be extremely comprehensive." (I got the idea from a Medium article[1].) The LLM will, indeed, stop and ask good questions. It often notices what I've overlooked. Works very well for me! [1] https://medium.com/@jordan_gibbs/the-most-important-chatgpt-prompt-55ef2bdc4d4a https://medium.com/@jordan_gibbs/the-most-important-chatgpt-...
tharkun__ 9mo ago
So like most junior to mid level devs ;) Claude does have this specific interface for asking questions now. I've only had it choose to ask me questions on its own a very few times though. But I did have it ask clarifying questions before that interface was even a thing, when I specifically asked it to ask me clarifying questions. Again, like a junior dev. And like a junior dev, it can also help to ask it to ask / check what its doing "mid-way", i.e. watch what it's doing and stop it, when it's running down some rabbit hole you know is not gonna yield results.

andrewmutz 9mo ago

The issues raised in this article are why I think highly-opinionated frameworks will lead to higher developer productivity when using AI assisted coding You may not like all the opinions of the framework, but the LLM knows them and you don’t need to write up any guidelines for it.

christophilus 9mo ago
Yep. I ran an experiment this morning building the same app in Go, Rust, Bun, Ruby (Rails), Elixir (Phoenix), and C# (ASP whatever). Rails was a done deal almost right away. Bun took a lot of guidance, but I liked the result. The rest was a lot more work with so-so results — even Phoenix, surprisingly. I liked the Rust solution a lot, but it had 200+ dependencies vs Bun’s 5 and Rails’ 20ish (iirc). Rust feels like it inherited the NPM “pull in a thousand dependencies per problem” philosophy, which is a real shame.
some-guy 9mo ago
I can vouch for this as someone who works in a 1.6 million line codebase, where there are constant deviations and inconsistent patterns. LLMs have been almost completely useless on it other than for small functions or files.

vivin 9mo ago

You can't get away from the engineering part of software engineering even if you are using LLMs. I have been using Claude Opus 4.5, and it's the best out of the models I have tried. I find that I can get Claude to work well if I already know the steps I need to do beforehand, and I can get it to do all of the boring stuff. So it's a series of very focused and directed one-shot prompts that it largely gets correct, because I'm not giving it a huge task, or something open-ended. Knowing how you would implement the solution beforehand is a huge help, because then you can just tell the LLM to do the boring/tedious bits.

teaearlgraycold 9mo ago
They’re good for getting you from A to B. But you need to know A (current state of the code) and how to get to B (desired end state). They’re fast typers not automated engineers.
ericmcer 9mo ago
seriously, I stopped agent mode altogether. I hit it with very specific like: write a function that takes an array of X and returns y. It almost never fails and usually does it in a neat way, plus its ~50 lines of code so I can copy and paste confidently. Letting the agent just go wild on my code has always been a PITA for me.
- vivin 9mo ago
  I've used agent mode, but I tell it not to go hog wild and to not do anything other than what I have instructed it to do. Also, sometimes I will tell it not to change the code, and to go over its changes with me first, before I tell it that it can make the changes. I feel the same way as you in general -- I don't trust it to go and just make changes all over the codebase. I've seen it do some really dumb stuff before because it doesn't really understand the context properly.

tschellenbach 9mo ago

I wrote this forever ago in AI terms :) https://getstream.io/blog/cursor-ai-large-projects/ https://getstream.io/blog/cursor-ai-large-projects/ But the summary here is that with the right guidance, AI currently crushes it on large codebases.

deleted 9mo ago
[deleted]

mstank 9mo ago

As the models have progressively improved (able to handle more complex code bases, longer files, etc) I’ve started using this simple framework on repeat which seems to work pretty well at one shorting complex fixes or new features. [Research] ask the agent to explain current functionality as a way to load the right files into context. [Plan] ask the agent to brainstorm the best practices way to implement a new feature or refactor. Brainstorm seems to be a keyword that triggers a better questioning loop for the agent. Ask it to write a detailed implementation plan to an md file. [clear] completely clear the context of the agent —- better results than just compacting the conversation. [execute plan] ask the agent to review the specific plan again, sometimes it will ask additional questions which repeats the planning phase again. This loads only the plan into context and then have it implement the plan. [review & test] clear the context again and ask it to review the plan to make sure everything was implemented. This is where I add any unit or integration tests if needed. Also run test suites, type checks, lint, etc. With this loop I’ve often had it run for 20-30 minutes straight and end up with usable results. It’s become a game of context management and creating a solid testing feedback loop instead of trying to purely one-shot issues.

AlexB138 9mo ago
This is essentially my exact workflow. I also keep the plan markdown files around in the repo to refer agents back to when adding new features. I have found it to be a really effective loop, and a great way to reprime context when returning to features.
- mstank 9mo ago
  Exactly this. I clear the old plans every few weeks. For really big features or plans I’ll ask the agent to create linear issue tickets to track progress for each phase over multiple sessions. Only MCP I have loaded is usually linear but looking for a good way to transition it to a skill.
  - AlexB138 9mo ago
    Ah, that's a great idea. I've just been having the agent add a Progress section to the plan files and checking things off as we work.

pron 9mo ago

> Here's a LLM literacy dipstick: ask a peer engineer to read some code they're unfamiliar with. Do they understand it? ... No? Then the LLM won't either. Of course, but the problem is the converse: There are too many situations where a peer engineer will know what to do but the agent won't. This means that it requires more work to make a codebase understandable to a human than it does to make it understandable to an agent. > Moving more implementation feedback from human to computer helps us improve the chance of one-shotting... Think of these as bumper rails. You can increase the likelihood of an LLM reaching the bowling pins by making it impossible to land in the gutter. Sort of, but this is also a little similar to claiming that P = NP. Having a an efficient way to reliably check if a solution is correct is not the same at all as a reliable way to find a solution. It's the theory of computation that tells us that it probably isn't. The likelihood may well be higher yet still not high enough. Even though theoretically NP problems are strictly easier than EXPTIME ones, in practice, in many situations (though not all) they are equally intractable. In fact, we can put the claim to the test: there are languages, like ATS and Idris, that make almost any property provable and checkable. These languages let the programmer (human or machine) position the "bumper rails" so precisely as to ensure we hit the target. We can ask the agent to write the code, write the proof of correctness, and check it. We'd still need to check that the correctness property is the right one, but if the claim is correct, coding agents should be best at writing code, accompanied by correctness proofs, in ATS or Idris. Are they? Obviously, mileage mauy vary dependning on the task and the domain, but if it's true that coding models will get significantly better, then the best course of action may well be, in many cases, to just wait until they do rather than spend a lot of effort working around their current limitations, effort that will be wasted if and when capabilities improve. And that's the big question: are we in for a long haul where agent capabilities remain roughly where they are today or not?

victorbjorklund 9mo ago

Biggest change to my workflow has been to break down projects to smaller parts using libraries. So where I in the past would put everything in the same code base I now break down stuff that can be separate to its own libraries (like wrapping an external API). That way the AI only needs to read the docs for the library instead of having to read all the code when working on features that use the API.

EastLondonCoder 9mo ago

I’ve ended up with a workflow that lines up pretty closely with the guidance/oversight framing in the article, but with one extra separation that’s been critical for me. I’m working on a fairly messy ingestion pipeline (Instagram exports → thumbnails → grouped “posts” → frontend rendering). The data is inconsistent, partially undocumented, and correctness is only visible once you actually look at the rendered output. That makes it a bad fit for naïve one-shotting. What’s worked is splitting responsibility very explicitly: • Human (me): judge correctness against reality. I look at the data, the UI, and say things like “these six media files must collapse into one post”, “stories should not appear in this mode”, “timestamps are wrong”. This part is non-negotiably human. • LLM as planner/architect: translate those judgments into invariants and constraints (“group by export container, never flatten before grouping”, “IG mode must only consider media/posts/*”, “fallback must never yield empty output”). This model is reasoning about structure, not typing code. • LLM as implementor (Codex-style): receives a very boring, very explicit prompt derived from the plan. Exact files, exact functions, no interpretation, no design freedom. Its job is mechanical execution. Crucially, I don’t ask the same model to both decide what should change and how to change it. When I do, rework explodes, especially in pipelines where the ground truth lives outside the code (real data + rendered output). This also mirrors something the article hints at but doesn’t fully spell out: the codebase isn’t just context, it’s a contract. Once the planner layer encodes the rules, the implementor can one-shot surprisingly large changes because it’s no longer guessing intent. The challenges are mostly around discipline: • You have to resist letting the implementor improvise. • You have to keep plans small and concrete. • You still need guardrails (build-time checks, sanity logs) because mistakes are silent otherwise. But when it works, it scales much better than long conversational prompts. It feels less like “pair programming with an AI” and more like supervising a very fast, very literal junior engineer who never gets tired, which, in practice, is exactly what these tools are good at.

tracker1 9mo ago

Just over the weekend, I decided to shell out for the top tier Claude Code to give it a try... definitely an improvement over the year I spent with Github CoPilot enabled on my personal projects (mostly an annoyance more than a help that I eventually disabled altogether). I've seen some impressive output so far, and have a couple friends that have been using AI generation a lot... I'm trying to create a couple legacy (BBS tech related, in Rust) applications to see how they land. So far mostly planning and structure beyond the time I've spent in contemplation. I'm not sure I can justify the expense long term, but wanting to experience the fuss a bit more to have at least a better awareness.

dmofp 9mo ago

I have a somewhat different take on this (somewhat captured in the post linked below). IMO, the best way to raise the floor of LLM performance in codebases is by building meaning into the code base itself ala DDD. If your codebase is hard to understand and grok for a human, it will be the same for an LLM. If your codebase is unstructured and has no definable patterns, it will be harder for an LLM to use. You can try to overcome this with even more tooling and more workflows but IMO, it is throwing good money after bad. it is ironic and maybe unpopular, but it turns out LLMs prove that all the folks yapping about language and meaning (re: DDD) were right. DDD & the Simplicity Gospel: https://oluatte.com/posts/domain-driven-design-simplicity-gospel https://oluatte.com/posts/domain-driven-design-simplicity-go...

dj_gitmo 9mo ago
Great post. I work on two large codebases. One is structured much like the example from the post, and the other is a mess. LLMs care much better at understanding the organized code.

__MatrixMan__ 9mo ago

I'm interested to see where we'll land re: organizing larger codebases to accommodate agents. I've been having a lot of fun taking my larger projects and decomposing them into directed graphs where the nodes are nix flakes. If I launch claude code in a flake devshell it has access to only those tools, and it sees the flake.nix and assumes that the project is bounded by the CWD even though it's actually much larger, so its context is small and it doesn't get overwhelmed. Inputs/outputs are a nice language agnostic mechanism for coordinating between flakes (just gotta remember to `nix flake update --update-input` when you want updated outputs from an adjacent flake). Then I can have them write feature requests for each other and help each other test fixtures and features. I also like watching them debate over a design, they get lazy and assume the other "team" will do the work, but eventually settle on something reasonable. I've been running with the idea for a few weeks, maybe it's dumb, but I'd be surprised if this kind of rethinking didn't eventually yield a radical shift in how we organize code, even if the details look nothing like what I've come up with. Somehow we gotta get good at partitioning context so we can avoid the worst parts of the exponential increase in token volume that comes from submitting the entire chat session history just to get the next response.

quinnjh 9mo ago
yeah this is an interesting approach, both for the context-partitioning but also for reproducibility and dependency pinning. i was toying with this before needing to run with just docker on a project. would be nice to find a tool that streamlines some of this
- __MatrixMan__ 9mo ago
  Re: dependency pinning, I put together a little write-up about that: https://gist.github.com/MatrixManAtYrService/6eaf50373448c0bc14acca31d69591b9 https://gist.github.com/MatrixManAtYrService/6eaf50373448c0b... You can use it as an alternative to `git bisect` where only you're only bisecting the history of a single subflake. I imagine writing a new test that indicates the presence of an old bug, and then going back in time to see when the bug was reintroduced. With git bisect, going back in time means your new test goes away too.

lnx01 9mo ago

LLMs are so good at telling me about things I know little to nothing about, but when when I ask about things I have expert knowledge on they consistently fail, hallucinate, and confidently lie...

dmoy 9mo ago
Feels like https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect https://en.wikipedia.org/wiki/Gell-Mann_amnesia_effect
- marcosdumay 9mo ago
  It was a very clear sarcastic reference to it. But it's still not completely right. LLMs are actually great to tell you about things you know little about. You just have to take names, ideas, and references from it, not facts. (And that makes agentic coding almost useless, by the way.)
llmslave2 9mo ago
I think you end up asking it basic questions about stuff you know little about, but much more complex/difficult questions for stuff you're already an expert in.
christophilus 9mo ago
I’ve found that they vary a huge amount based on the subject matter. In my case, I have noticed the opposite of what you observed. They know a lot about the web space (which I’ve been in for around 25 years), but are pretty bad (though not useless) at esoteric languages such as Hare.
- drcxd 9mo ago
  Obviously, since the training material for such esoteric languages is scarce. (That's why they are esoteric!) So by definition, LLM will never be good at esoteric languages.
throw-12-16 9mo ago

blauditore 9mo ago

It's like people are rediscovering the most basic principles: E.g. that documentation ("prompt library") is usecho, or that well-organized code leads to higher velocity in development.

hu3 9mo ago
if that's what it takes for more people to write tests, then so be it

laser9 9mo ago

> This is the garbage in, garbage out principle in action. The utility of a model is bottlenecked by its inputs. The more garbage you have, the more likely hallucinations will occur. Good read but I wouldn't fully extend the garbage in, garbage out principle to the LLMs. These massive LLMs are trained on internet-scale data, which includes a significant amount of garbage, and still do pretty good. Hallucinations are due to missing or misleading context than from the noise alone. Tech debt heavy code bases though unstructured still provides information-rich context.

patcon 9mo ago

Is it not the case that "production level code" coming out of these processes makes the whole system of coder-plus-machine weaker? I find it to be a good thing that the code must be read in order to be production-grade, because that implies the coder must keep learning. I worry about the collapse in knowledge pipeline when there is very little benefit to overseeing the process... I say that as a bad coder who can and has done SO MUCH MORE with llm agents. So I'm not writing this as someone who has an ideal of coding that is being eroded. I'm just entering the realm of "what elite coding can do" with LLMs, but I worry for what the realm will lose, even as I'm just arriving

spullara 9mo ago

Using AugmentCode's Context Engine you can get this either through their VSCode/JetBrains plugins, their Auggie command line coding agent or by registering their MCP server with your local coding agent like Claude Code. It works far better than painstakingly stuffing your own context manually or having your agent use grep/lsp/etc to try and find what it needs.

avree 9mo ago

Why do none of these ever touch on token optimization? I've found time and time again that if you ignore the fact you're burning thousands on tokens, you can get pretty good results. Things like prompt libraries and context.md files tend to just burn more tokens per call.

hobofan 9mo ago

> Aside: Why are LLMs good at greenfield? I have the complete opposite experience, where once some patterns already exist 2-3 times in the codebase, the LLMs start to accurately replicating them instead of trying to solve everything as one-off solutions. > You can’t be inconsistent if there are no existing patterns. "Consistency" shouldn't be equated to "good". If that's your only metric for quality and you don't apply any taste you'll quickly end of with a unmaintainable hodgepodge of second-grade libraries if you let an LLM do its thing in a greenfield project.

throw-12-16 9mo ago

There is no way this is economical. Burn through your token limit in agent mode just to thrash around a few more times trying to identify where the agent "misunderstood" the prompt. The only time LLM's work as coding agents for me is tightly scoped prompts with a small isolated context. Just throwing an entire codebase into an LLM in an agentic loop seems like a fools errand.

eurekin 9mo ago

If you're interested in the large codebase... The best I found so far are extended context models. Using newest Nemotron3 nano, you can put a 1m tokens (about 3 ish megabytes of text) of pure code dump (I use repomix --style markdown) and ask around. That's been one of the biggest wow moments I had with LLMs so far. Much better experience than any RAG I used

Simplita 9mo ago

One thing that helped us as codebases grew was separating decision-making from execution. Let the model reason about intent and scope, but keep execution deterministic and constrained. It reduced drift and made failures much easier to debug once context got large.

ColinEberhardt 9mo ago

“When an LLM can generate a working high-quality implementation in a single try, that is called one-shotting. This is the most efficient form of LLM programming.” This is a good article, but misses one of the most important advances this year - the agentic loop. There are always going to be limits to how much code a model can one-shot. Give it the ability to verify its changes and iterate, massively increase its ability to write sizeable chunks of verified and working code.

jukkat 9mo ago

Put every detail into CLAUDE.md and after a while CC starts to forget/ignore what it’s been told. I’d like to see dynamic task-specific context building. Write a prompt and the model starts to collect relevant instructions. Also a review loop to check that instructions were followed.

Ayanonymous 9mo ago

I'm still learning about how LLMs can be used in coding, but this article helped me understand the importance of giving clear instructions and not relying too much on automation. The point about developers still needing to guide the model really makes sense. Thanks for sharing this!

eddywebs 9mo ago

How about adding MCP support to large code bases to provide RAG based context to LLMs, ive been playing with this idea with some good results.