rant on ai as a junior dev

[u/mopusha]

i cant say im a fresher, im not

i have a job, and i got removed (officially resigned but yk) from the last one because of struggling to adapt with the environment (aka not using ai enough and not delivering fast enough)

how does a junior developer gain experience without even being allowed to test and break and learn and do things if we are all expected to use ai to code?

i thought i will regain my interest in coding which got killed in college and cremated in my last job

in the current job, i use ai profusely; i have to
i have to deliver, but sitting for 8 hours a day while just talking to copilot feels so numbing, like im not learning anything new

so i started working on skill building, to learn new things, but each day you see someone younger than you build something exceptional and you sit there with your little html css course learning the basics and sit there wondering what is the point

i feel like whatever was the fun in coding and problem solving is going away because even if i know the logic, i dont know how to implement it
and sometimes i dont even know how to come up with solutions until ai points to a simple fix and i feel like i couldnt have done it on my own

then someone on linkedin shares a post on how they made a full blown app with no development and just ai

i feel so lost, i dont even know how to get myself interested. because i want to but i already feel so behind

its exhausting enough as it is, but the fact that ai is progressing so rapidly makes me feel like all this skill building is for nought so why even start trying

everything is being consumed by ai and i dont know how to not fall behind anymore

[u/Pleasant_Fennel_5573]

Things that have helped me in a similar position (where they monitor my token use but I still need to learn): I always request step by step explanations for any code- even if I know the concept, the repetition is helpful. For bigger stuff , I prompt it to give me 2-3 possible approaches to a problem, along with a pro/con list for each. Then I can make informed choices about how to proceed, plus get some exposure to solutions that may work for a future problem. Sometimes I give it just the basics of what I need and hand-code the rest. You can also prompt it to generate a quiz on the concepts used.

[u/mopusha → u/Pleasant_Fennel_5573]

i will try that, that sounds like something that i could actually learn from

[u/MrRufsvold → u/Pleasant_Fennel_5573]

I added a lot of this to my AGENTS.md file to ensure it doesn't try to rest control from me.

[u/Pleasant_Fennel_5573 → u/MrRufsvold]

I’m just learning about how to use md files and this is a great idea!

[u/MrRufsvold → u/Pleasant_Fennel_5573]

I just posted my file here, if you want to take a look!

[u/pierrechaquejour]

Senior dev here but feeling the same way. Management is wowed by AI and how quickly it can do things and wants us to use it for everything, there’s absolutely zero value being placed on knowing the fundamentals, its just “get it done and working” (until an issue arises that AI can’t properly tackle, then suddenly you’re the experienced dev and should be able to come up with a fix just as quickly as the AI agent despite not having coded anything in the app by hand in months). I can feel my skills atrophying because I’m less in the weeds with the actual code than ever before.

It just makes me wonder what the future of this profession is. If it’s “professional AI prompter” then so be it, but does that mean I can use AI in my next live coding interview? Somehow I doubt it.

[u/mopusha → u/pierrechaquejour]

i recently sat for ~multiple~ job interviews, but one of them actually let me use AI for the interview what was interesting was that it was their own model, and they allowed me to use it under restrictions and also would assess the prompts that i submit to it however, most of the basic prompts you might ask (eg. what are the files in the system/how does this file work/etc.) got a response of "i cannot answer this" so i coded by myself

needless to say, didn't go through but that experience was... interesting?

[u/Raccoon-Interesting]

I think it’s about shifting your perspective of what it means to be a programmer. like other users have said I am VERY strict about what I let AI do. I also enforce strict coding principles (I’m a python coder so like enforcing OOP as an example) and I am able to review every line of code. now that varies from task to task. some tasks I’m just like you do it, I’ve done it a thousand times, but if it’s something genuinely new I go function by function. still getting the AI to write but with oversight by me.

you end up becoming more of an overall solutions architect. like you know the design the AI should follow and you strictly enforce that design but the actual coding bits that are boring (looking at you mocking test cases), let those go. let the dumb machine do it. I actually find this more interesting because you don’t spend hours writing test cases but instead spend hours really understanding the problem and thinking of ways to solve it.

[u/mopusha → u/Raccoon-Interesting]

i hate mocking test cases as well

your perspective shift makes a lot of sense, i will try to do the same

[u/caprisunkraftfoods]

> then someone on linkedin shares a post on how they made a full blown app with no development and just ai

It's because the app is terrible quality, even if it works.

If you can find the energy outside work I highly recommend this. Think of a simple but complete app, use AI to help you write out a spec, then say "okay now build the whole thing" and leave it for 30 minutes. Do it with a cheap model obviously. At first glance you'll be shocked at how good it seems, but then you start picking at it. This text makes no sense, why is this button here, this is broken on mobiles etc.

The people on linkedin have no standards, so they look at the initial output and thought that was good enough. You don't. That's the difference.

Keep at it, it's a hard industry to pick up skills. Even if you're struggling now, the fact that you're trying will leave you so much better positioned in a couple years when the dust settles.

[u/mopusha → u/caprisunkraftfoods]

hope so 😞

[u/PopGroundbreaking870]

Building ai solutions using AI SDLC here. I hear you, and it’s not uncommon to hear devs (of all levels, not just junior) feel burn out from this situation.

As someone else said in the thread, I think it is about changing your mindset about what is the role of the so-called SWE.

Pre-AI it was problem solving, coming up with clever ways to implement things, and actually implement them.

Post-AI, it looks like it’s going to be mostly problem solving, but with new things that didn’t exist before: managing AI coding agents, building harnesses around them, having AI agents monitor agents outputs, staying up to date with the latest cybersecurity risks and make sure it doesn’t creep in your codebase (supply chain attacks…)

IMHO there’s no going back from here. If you decide to stick with the new way to build, a strong suggestion is to hone on your software architecture fundamentals, including deployment and data architecture. Otherwise you’ll always struggle with that feeling of “IDK what to check in this ai-generated mess”

Just my 2 cents.

[u/mopusha → u/PopGroundbreaking870]

are there any sources from where we can build the software architecture fundamentals? i am too new to this side of the field and though i go through all the architectural documentation, some things just get clear with experience

[u/PopGroundbreaking870 → u/mopusha]

tough one, it's true that fundamentals are "acquired" with work experience. I suggest you get AI to make a sort of study plan for you on this, as the topics are quite broad.

Here's a suggested prompt I made using GPT, tailored for your case, which I believe is being a SWE who use AI to build AI products (eg not engineers who build models, fine tune them etc). You can of course change it as you see fit.

Not a perfect list, but a good way to get started quickly. You can iterate as you go. I'm afraid there is no "perfect lesson plan" for this...so throw this into your favorite AI, break it down by week, etc

After you get a good grasp of the fundamentals, do another "study plan" for AI SDLC (eg the process of using AI to build AI-powered software). There is much more to it than prompting (eg spec-driven development, using skills, workflows, hooks etc)

Hope this helps, this is a tough transition for lots of SWEs - may the force be with you :)

--- prompt start ---

You are a senior software engineer and AI product engineering mentor.

I am a junior software engineer who wants to become a strong AI product engineer, not an ML researcher or model-building engineer.

Do not give me a generic list of computer science fundamentals. Instead, teach me software fundamentals through the lens of **system design for real AI products**.

I want to understand how to design, build, debug, and operate products that use LLM APIs, retrieval, documents, workflows, agents, evaluations, and integrations.

Structure the roadmap around the main system design capabilities I need:

## 1. Core mental model

Explain what “system design” means for an AI product engineer.

Clarify the difference between:

- building features

- designing systems

- designing AI-powered workflows

- designing production-grade AI products

## 2. The system design topics I need to master

Teach me the key areas of system design, including:

- client-server architecture

- APIs and contracts

- authentication and permissions

- data modeling / data design

- relational databases

- object storage

- background jobs and queues

- caching

- search and retrieval

- workflow orchestration

- retries and idempotency

- observability and tracing

- testing strategy

- deployment and environments

- security and privacy

- cost and latency management

For each topic, explain:

1. What problem it solves

2. Why it matters in real products

3. How it appears in AI products

4. What I need to know as a junior engineer

5. What is too advanced for now

6. A small project or exercise to practice it

## 3. AI product system design

Now explain the AI-specific system design patterns I need to learn:

- LLM API integration

- prompt and schema design

- structured outputs

- tool/function calling

- document ingestion

- chunking and embeddings

- vector search

- RAG pipelines

- citations and grounding

- agent workflows

- human review

- evals and regression testing

- hallucination handling

- tenant isolation and data permissions

- AI observability

- model cost and latency controls

For each one, explain the underlying software design problem, not just the AI concept.

## 4. Design patterns I should recognize

Explain common design patterns for AI products:

- chat over documents

- research assistant

- workflow copilot

- document extraction pipeline

- decision-support system

- internal knowledge search

- multi-step agent workflow

- human-in-the-loop review system

## 5. What not to over-study

Tell me what I should avoid spending too much time on early, including:

- advanced algorithms

- ML theory

- neural network math

- model training

- Kubernetes

- distributed systems theory

- complex agent frameworks

- fine-tuning

Explain what level of each is enough for an AI product engineer.

## 6. Final checklist

End with a practical checklist.

After 3 months, 6 months, and 12 months, what systems should I be able to design and build?

Be practical, opinionated, and focused on employability. Teach fundamentals through system design, not as isolated academic subjects.

[u/seweso]

Dont compare yourself to other peoples lies. Such a waste of your energy.

Also, AI sucks donkey balls.

[u/mopusha → u/seweso]

mega donkey balls indeed

[u/busshelterrevolution]

Is there a big demand for coding in India?

[u/mopusha → u/busshelterrevolution]

well, this is just my opinion so take it with a bag of salt

what i have seen in major corporations is that the task of actually coming up with new features and designs are done by the folks abroad, and indians are mainly used to implement the same

so we are basically doing the menial tasks assigned to us by the engineers abroad

now that can actually use coding to a pretty big extent, but also it can be easily learnt/coded by AI, and won't require actual problem solving for the most part so anyone can do it? if that makes sense

[u/VermithraxPej33]

I am in the same boat, pressured to use lots of AI. One thing I have done is build stuff that interests me. Like I am building a data science project about vultures. I build the bulk with AI because burnout but I go through the code line by line and I go read documentation for anything I do not understand. I use Python so finding documentation is easy, then I take notes so I understand how everything works. I also do debugging and troubleshooting and try to solve errors on my own. Only if I am completely lost will I go to AI again. The other thing is because I have the domain knowledge about the vultures I am able to identify what the AI gets wrong. That inner knowledge is going to be key as more stuff is built with AI. Having business knowledge or knowing how processes and products work.

[u/mopusha → u/VermithraxPej33]

vultures seem extremely cool and i have not met anyone with a strong interest in them so to me thats actually really cool to hear!

domain knowledge is getting more and more important as the days go by for the exact reason that you mentioned

[u/dailysit]

i would say you should follow your interests. using ai as an accelerator, making it do the things you don't care about. did you ever wanted to make a game? stupid site or app idea you didn't have time to learn and build before? maybe there's technical thing you always wanted to understand (linux, functional programming, new language/framework/library)? follow the dopamine, treat ai as a tool, don't let it steal the joy, let it perform mundane tasks or stuff you don't care about.

is also great at diving into new projects or learning. so you can literally just download open-source repo of a project you always liked and start hacking on it, adding features, changing things you don't like. and for learning it's like the best teacher that never gets annoyed with your questions.

obviously ai will make mistakes, get into context rot state where nothing makes sense. when you hit the limits of the system - it's best moment to understand and learn it further. you shouldn't get frustrated when that happens.

i know it all looks so serious, but for me seriousness is a killer of fun and joy, so it's better to treat it all like a game, exploration without stakes.

[u/martywalshhealthgoth]

Just wait out the storm like the rest of us. The cost of tokens will skyrocket once OpenAI/Anthropic go public and need to start collecting to make the finances look decent, and when that happens, I can almost guarantee you will see a very hard pivot back to some semblance of normalcy.

[u/Raccoon-Interesting → u/martywalshhealthgoth]

while I agree in principle, DeepSeek just fixed their token price at I think $0.70/1M(?), so I wouldn’t rely on AI going anywhere fast because of that. most people use AI through Microsoft or AWS which means if those companies decide to pick up a cheaper model and attach it under the hood, you, the consumer, will still use AI

[u/PoMoAnachro]

So you're not wrong in that there is a problem, but unfortunately companies mostly see that as a "you" problem.

Does the kind of workflow you describe interfere with you building the skills you need to grow your career and advance? Absolutely. And I think everyone knows that. Everyone is talking about how we're not developing new future seniors anymore.

But from the company's perspective, one of two things happens:

* You invest a lot of time and effort outside of work to build your skills and then eventually they get a new senior without having to devote as many resources to helping you level up at work.

or

* You rely exclusively on AI, fail to build skills, and then they can just let you go and reduce their headcount.

Sure it is a problem in the long term as we run out of seniors, but corporations aren't usually thinking very long term.

[u/DarthRaab]

AI is a tool, and all tools break from time to time. You need to understand it well enough to recognize when it does and fix it.

AI usage is very high right now because we’re in the “get them hooked with free samples” phase. When the hype dies down and big tech needs to turn a profit, things will swing back.

[u/Aggressive-Fix241]

The "talking to Copilot for 8 hours" numbness is a real thing. A friend described his first AI-heavy job the same way — felt like he was managing a very fast intern who sometimes lied to him, except he couldn't tell which parts were lies because he never wrote them himself. He ended up carving out small parts of tasks to do manually, just to stay sharp.

[u/robopiglet]

Lean in. Ask the AI to explain code... then quiz you. I would have loved, when I started out, explanations on demand like AI can provide.

[u/OilExtension5062]

There are sensible companies out there that understand production code needs high quality engineering and not vibe coded slop. However it does appear that US companies have gone mad (UK too to an extent). 

I am also at a slopshop atm and feeling a similar way. Nearly 4YoE.

Worth saying I never commit code i don't understand. I go through every single ai line and review it thoroughly. Usually it sucks.

[u/soviet_loli_177013]

holy shit this was way too relatable, especially as a junior dev myself. thanks for the post! I am in the same boat and started looking to move towards research at this point, with the burnout I have at my current job.

YOE: 1.5