The way I see it you should calibrate the way you work with LLMs based on how confident you are on that specific area, and if it's your responsibility to own/understand it. Here's how it feels for me:
* Learning stage: 0.5x - 1x. I change my system prompt to teacher mode, taking the productivity hit for actually learning the system/tool pays off dividends later. I change my system prompt to "teacher mode" and slowly loosen it as I get more confident.
* Working-knowledge: 2x - 3x. Once I am ramped up enough I feel like I can get a decent productivity boost. Most of the time is spent at the planning stage. This is my mode for areas I don't really own or care about, just need to get work done.
* Mastered: 10x+ I have been doing web front end for 12+ years, I can quickly review plan/implementations and for my initial prompt I already know most of what I want built.
Some domains don't have the established regularity of architecture that it takes to achieve 10X efficiency. Two I can think of off the top of my head are embedded systems, with a variety of sensors, outputs, processing power, and memory, and novel protocols like ATProto, for which training data is thin on the ground.
This reminds me of themes I recently saw in [Harness Engineering is not Enough: Why Software Factories Fail](https://www.youtube.com/watch?v=Ib5GBkD555M) (Warning: the last 3 slides seem like an advertisement). One thing I liked is how Dex has a little graphic he glossed over showing software development is
- 25% planning & aligning with other teams
- 25% coding
- 25% testing/verifying
- 25% code review/rework
One argument was that agentic coding speeds up that coding part a bunch. So maybe there's 2x speedup in coding. But that's only a small speedup in the totality of everything software engineers do.
I use CC to do the testing and verifying in multiple deployment environments. It’s faster at things like coming up a plausible test payload or ten, querying a bunch of metrics from datadog, querying and verifying state from a db before and after testing, scanning all the deployed logs for relevant errors. For me that has been more of an uplift than the coding. I still feel like communication it’s worst at. So writing tickets, getting a decision out of a group, documenting only the important things, etc. it’s still not that good at
The other hard to measure part is that I see/hear a lot of LLM usage going towards dev work that was never prioritized before.
Suddenly devs who were cranking out features with no interest in infrastructure are attacking giant refactors to make the code more understandable to the LLM.
Other devs are using LLMs to build themselves quality of life SDLC tools completely separate from the core code base.
The quality of my bash scripts is 100x what they used to be. Proper help messages, flag parsing, functions, internal variables, using sed all the time (I could never wrap my head around that).
Of course, the main issue is that they’re completely undebbugable now. My bash scripts used to be a sequential list of commands, now they’re 500 lines of variable laden functions.
Is my life any better? Dunno. But it’s satisfying (until there’s a bug)
Just one more data point but this is what I’m seeing too. The extreme ‘ruthless’ prioritization has stopped and devs (who want it) have breathing room to make the enhancements they’ve been wanting all along. Turns out what is good for AI is also good for regular old school SDLC. All you have to do is say it will make AI more effective.
You still have to know what you’re doing. That came from years of doing it by hand. Where is that necessary experience going to come from for the current and future generations of juniors?
> As for documentation, I've found this simple instruction to vastly improve LLMs' output:
> Never write READMEs, docstrings, or comments. I will write those myself later. And yes, I really mean this.
This is quite validating as I came to the exact same conclusion myself. We’re required to use an LLM for every task at work that touches code†, and I was really struggling to get Claude to stop with the long waffly comments that reiterate the next few lines of code in 3x as many characters, making contextless references to subtasks in whatever harness du jour we’re using this week.
No amount of examples or explanation of what I wanted would make it stop. And then I realised of course, I’m asking something which has no concept of meaning (or, indeed, anything) to only add meaningful comments. More fool me I guess.
Of course, it’s ultimately pointless given all of my colleagues are regularly opening PRs with more comments than code anyway. 80% of my code review responses these days are just increasingly exasperated “pointless comment, please remove”.
† This is just as infantilising as it sounds, by the way
+10, I can't stand AI-generated comments. Half the time they are paragraph-length translations of every 5 lines of code into English prose, or just regurgitating whatever prompt they were given. When they actually try making high level comments they are worse than useless. It's really hard to put into words exactly why. It's something about how they are very verbose, but at the same time vague and stated in a slightly-off manner. Or they will introduce new terminology not reflected in the code's nomenclature, sometimes terms that are just synonyms for terms that the code already uses. Something about it makes me instantly distrustful of any information the comment may actually convey.
locally correct and globally incoherent is how they seem to me. I, and a number of folks I have talked to, find some of them incomprehensible. Like each sentence makes sense, but the overall thing does not.
You can create a PostToolUse hook [0] which will automatically chastise Claude to shrink comments to one line. It's not perfect but better than the sea of prose it tends to generate by default. Bonus is that it would apply to your colleagues as well!
>As such, I use LLMs mainly to produce a rough draft of the code which I then iterate on heavily, at least until I like the general structure
Tell me you just started with agentic programming without telling me you just started using agentic programming.
Look, don't get me wrong: new folks learning tech should absolutely write articles about it! However, any sort of claims (especially the bigger/grander ones) should be taken with giant helpings of salt ... just as you would take pronouncements from a junior dev about how all software should be buil.
Whatever this guy knows, it's clear from the article he's relatively new to the tech. That makes many of his conclusions ... suspect.
This is a dated take to me. I think the next stage in unlocking productivity is so called "loop engineering"; figuring out how to effectively not read all the code while ensuring quality. To me that means implementing statistical quality control and formal methods.
Before I get there I have to figure out how to reliably audit plan adherence. The problem is that when the specs are in natural language, as they are, you need a fallible LLM to verify it.
I already convert from multipliers to percentage of increase, so when someone claims 10x they very likely mean +100% productivity, and here 2x means +20% productivity, which seems about right. Nobody that was normally productive before LLMs has suddenly 10x'ed their output now.
The problem is that 20% productivity when it comes to generating code, really doesn't translate in 20% productivity increase overall, when you take into account the fact that the code quality is worse, the fact that writing code is actually not the majority of your time spent, and that people get burnt out from the usage.
I found that latest codes don't write comments in code by default. And when they do, they write stupid shit like "This was code that did X, it was now removed".
You have to explcitly prompt them to write comments in code. They are still useful for you, the user. But are arguably useful for the model, too, given how many of them (especially Claude) only reads small chunks of files. So I'd rather have code comments than it reproducing a picture from incomplete data.
... the official return of pair programming! The best part: LLM's save you from having to argue with a human. Added bonus: now you get to master two tools instead of one.
My observation is that AI tools generally make people /less/ productive. They are more productive in that they produce more stuff, but they are less productive in that the stuff they are producing has lower inherent value delivery. The primary place where I see this in software teams as a Product person is in document creation. If you are using an AI tool to produce internal documents, this is likely a net-negative value activity that actually reduces the overall productivity of the team. Why? Because writing is thinking. By handing off document creation to an LLM for a document that's supposed to communicate important details between teams (e.g. technical design docs, requirements docs, strategy docs) you are actually handing off thinking, or rather handing off not-thinking as AI tools cannot "think". The outcome of is poorly "thought out" slop that generates more work for everyone involved to resolve /or/ everyone simply ignores the document and the previous processes stop being useful.
If anything, I'd say AI tools in /most/ enterprises as people are trying to use them now are at least a -30% of productivity. If used correctly for taking a human-written/thought requirements doc, converting it into an interactive prototype that can be critiqued and ultimately included as part of the solution alignment within the requirements doc, and then is handed to an engineering team that is effective at using humans and AI to produce code, then it's probably a benefit. But most businesses lack the internal rigor, quality culture, and data governance to support properly applying AI tools in a high context manner internal to their business.
If 60x was a thing, we would see many fantastic products appearing on the market, every product would have improved or goten major new interesting features. Yet it hasn't really happened.
* Learning stage: 0.5x - 1x. I change my system prompt to teacher mode, taking the productivity hit for actually learning the system/tool pays off dividends later. I change my system prompt to "teacher mode" and slowly loosen it as I get more confident.
* Working-knowledge: 2x - 3x. Once I am ramped up enough I feel like I can get a decent productivity boost. Most of the time is spent at the planning stage. This is my mode for areas I don't really own or care about, just need to get work done.
* Mastered: 10x+ I have been doing web front end for 12+ years, I can quickly review plan/implementations and for my initial prompt I already know most of what I want built.
1x == my speed before AI
- 25% planning & aligning with other teams
- 25% coding
- 25% testing/verifying
- 25% code review/rework
One argument was that agentic coding speeds up that coding part a bunch. So maybe there's 2x speedup in coding. But that's only a small speedup in the totality of everything software engineers do.
Amdahl's Law should be familiar to anyone with a 4y computer science/engineering degree. Why aren't they applying it to their own throughput?
Suddenly devs who were cranking out features with no interest in infrastructure are attacking giant refactors to make the code more understandable to the LLM.
Other devs are using LLMs to build themselves quality of life SDLC tools completely separate from the core code base.
Plenty of other examples of this.
Of course, the main issue is that they’re completely undebbugable now. My bash scripts used to be a sequential list of commands, now they’re 500 lines of variable laden functions.
Is my life any better? Dunno. But it’s satisfying (until there’s a bug)
Well, that means the quality actually dropped then :). Looking impressive isn't equal to quality, understandability and reliability is
> Never write READMEs, docstrings, or comments. I will write those myself later. And yes, I really mean this.
This is quite validating as I came to the exact same conclusion myself. We’re required to use an LLM for every task at work that touches code†, and I was really struggling to get Claude to stop with the long waffly comments that reiterate the next few lines of code in 3x as many characters, making contextless references to subtasks in whatever harness du jour we’re using this week.
No amount of examples or explanation of what I wanted would make it stop. And then I realised of course, I’m asking something which has no concept of meaning (or, indeed, anything) to only add meaningful comments. More fool me I guess.
Of course, it’s ultimately pointless given all of my colleagues are regularly opening PRs with more comments than code anyway. 80% of my code review responses these days are just increasingly exasperated “pointless comment, please remove”.
† This is just as infantilising as it sounds, by the way
[0] https://github.com/chrisvariety/branch-fiction/blob/deb37f2b...
Why not just write the code in the prompt so LLM can paste it.
Tell me you just started with agentic programming without telling me you just started using agentic programming.
Look, don't get me wrong: new folks learning tech should absolutely write articles about it! However, any sort of claims (especially the bigger/grander ones) should be taken with giant helpings of salt ... just as you would take pronouncements from a junior dev about how all software should be buil.
Whatever this guy knows, it's clear from the article he's relatively new to the tech. That makes many of his conclusions ... suspect.
Before I get there I have to figure out how to reliably audit plan adherence. The problem is that when the specs are in natural language, as they are, you need a fallible LLM to verify it.
I already convert from multipliers to percentage of increase, so when someone claims 10x they very likely mean +100% productivity, and here 2x means +20% productivity, which seems about right. Nobody that was normally productive before LLMs has suddenly 10x'ed their output now.
The problem is that 20% productivity when it comes to generating code, really doesn't translate in 20% productivity increase overall, when you take into account the fact that the code quality is worse, the fact that writing code is actually not the majority of your time spent, and that people get burnt out from the usage.
I found that latest codes don't write comments in code by default. And when they do, they write stupid shit like "This was code that did X, it was now removed".
You have to explcitly prompt them to write comments in code. They are still useful for you, the user. But are arguably useful for the model, too, given how many of them (especially Claude) only reads small chunks of files. So I'd rather have code comments than it reproducing a picture from incomplete data.
Weird… I would have said this was how it was about 2 years ago, but no way in 2026.
If anything, I'd say AI tools in /most/ enterprises as people are trying to use them now are at least a -30% of productivity. If used correctly for taking a human-written/thought requirements doc, converting it into an interactive prototype that can be critiqued and ultimately included as part of the solution alignment within the requirements doc, and then is handed to an engineering team that is effective at using humans and AI to produce code, then it's probably a benefit. But most businesses lack the internal rigor, quality culture, and data governance to support properly applying AI tools in a high context manner internal to their business.
Take that, you mere 10x-ers! Your days are toast!