Vibe Coding vs SOLID Principles: The New Battle for Code Quality
Vibe coding may be trendy, but SOLID principles are essential. This guide explains how to combine traditional software engineering knowledge with AI tools for optimal results.
Hello and welcome back to my cozy newsletter, where we discuss all the things starting from engineering and ending with the AI world and management. Today I wanted to talk a little about SOLID principles, which might be refreshing for seasoned professionals who already started to forget about them but also for new people who join IT these days.
Short disclaimer – yes, this might help you pass the tech interview, but it will also help you become a better engineer.
So why exactly did I decide to remember these principles in the first place? Because of AI. Yes, the new era of programming is coming – you've probably already even tried it – vibe coding.
And while vibe coding isn't exactly a bad thing (our tools evolve), you probably know I've been a little bit skeptical about AI coding all along. If you recall an episode from last year "How AI Coding Tools Might Set You Up for Failure," I even came up with some sort of theory why AI-generated code will inevitably be below any average standard.
But here's the twist: I'm not here to bash AI again. I'm here to remind you that some things never change, even when everything else does.
The Shifting Interface of Development
Let's not kid ourselves. What's happening in our industry right now isn't some magical democratization of programming (though it has a vibe now). The interface is changing, but the hard parts remain hard.
Most developers I see are just typing "write me a function that does X" and calling it a day. Then they wonder why the AI gives them garbage.
Surprise! Crafting a good quality prompt that outputs exactly what you need is just as difficult as writing good code. Maybe even harder. I've watched one senior developer (yes that was me) spend 30 minutes in a loop of "no, not like that... I meant this... no, change this part..." when they could have just written the damn function themselves in 5 minutes.
The knowledge didn't disappear – it just moved upstream. But most people aren't bringing their expertise to their prompts. They're treating AI like a magic oracle rather than a tool that needs precise guidance.
I've reviewed codebases where it's painfully obvious which parts were AI-generated without architect oversight. Inconsistent naming conventions. Weird design patterns that don't align with the rest of the system. Solutions that solve the immediate requirement but will collapse under the next feature request.
Why Experience Still Trumps AI Assistance
Here's what the AI evangelists won't tell you: garbage in, garbage out still applies. Inexperienced developers will create inexperienced prompts.
They won't know why their AI-generated authentication system breaks when the user requirements change. They won't understand why their "perfect" microservice architecture crumbles under real-world load. They'll blame the tool, not their instruction.
And I've seen it happen already. The juniors who think Cursor or GitHub Copilot will make them seniors overnight. The startups building their entire infrastructure on LLM-generated boilerplate.
We're creating a generation of developers who can't debug because they don't understand what they deployed. "It doesn't work" becomes the new normal, followed by "Hey AI, please fix it" instead of actual troubleshooting.
Now, experience and knowledge matters more than ever. A seasoned developer doesn't just know what to build – they know what will break. They understand the edge cases, the scalability concerns, the maintenance nightmares that hide beneath the surface of seemingly elegant solutions.
AI can't replace that intuition, nor can it replace the knowledge. Not now, not soon.
Knowledge-Infused Prompting Is the New Coding
If you're up for vibe-coding or even just using regular copilot, you need to bring your full arsenal of experience, intuition, and knowledge into every prompt to achieve acceptable code quality.
This isn't as simple as it sounds. You have to understand which principles to explicitly mention. Which design patterns to specify. Which edge cases to anticipate. It's a meta-skill that's emerging – the ability to encode your software engineering expertise into natural language that an AI can properly interpret.
Also, let's be honest: there are still areas where it's better to read a proper book than to ask ChatGPT. Some concepts are too nuanced, too context-dependent for an AI to really nail. The fundamentals of distributed systems. The intricacies of memory management. The subtle art of proper abstraction.
Experienced developers are theoretically positioned to reap the benefits of AI coding – but there's a massive catch. Only those who master this new skill of proper prompting will actually see the gains.
What are Great Developers Nowadays?
It's not enough to be a great developer anymore. You need to be a great developer who can also translate your expertise into prompts that guide AI tools effectively. It's like learning a new language where the syntax is English but the semantics are pure software engineering.
And here's the kicker – most senior developers I know are struggling with this transition. They're used to expressing their knowledge in code, not in natural language instructions to an AI. The mental model is different. The feedback loop is different. The debugging process is entirely different.
At the same time, people who haven’t mastered software development in a first place are flooding projects with AI-generated junk code, creating a ticking time bomb of technical debt. And the most hilarious part here: companies are building on that shaky foundations without even realizing it.
The tools supposedly transforming programming are actually creating two new classes of developers – those who can effectively wield AI through expert prompting, and those who produce increasingly problematic codebases that will eventually need rescue.
I predict the next few years will see a surge in "rescue projects" where the former group has to salvage systems built by the latter. The demand for truly experienced developers who understand both fundamentals AND how to properly direct AI tools will skyrocket.
SOLID: The Cornerstone You Can't Ignore
Having said all that, I just want to provide a truly powerful set of principles that first emerged 21 years ago. They still remain valid, practical, and most importantly, can help you better understand what I meant with everything said in the first chapter.
Look, I know many of you are rolling your eyes right now. "SOLID principles? Again? Boring!" I get it. These aren't new, and if you've been in the industry for more than fifteen minutes, you've heard them repeated like a mantra. But hear me out.
These principles are good example of the exact knowledge you need to encode in your prompts if you want AI to generate anything remotely maintainable.
They're the mental models that separate developers who can leverage AI effectively from those who just create tomorrow's technical debt today.
So, let's get back to basics. Here's the latest interpretation of SOLID principles by Robert C. Martin (nothing new, but reduces the likelihood of misperception):
SRP) The Single Responsibility Principle.
Gather together the things that change for the same reasons. Separate things that change for different reasons.
OCP) The Open-Closed Principle.
A Module should be open for extension but closed for modification.
LSP) The Liskov Substitution Principle.
A program that uses an interface must not be confused by an implementation of that interface.
ISP) The Interface Segregation Principle.
Keep interfaces small so that users don’t end up depending on things they don’t need.
DIP) The Dependency Inversion Principle.
Depend in the direction of abstraction. High level modules should not depend upon low level details.
Simple, right? Yet I can't count how many systems I've seen struggle because developers – human or artificial – ignored these fundamentals.
All I can say it’s a battle-tested heuristics that save you from your future self.
Applying SOLID in an AI-Assisted Workflow
One particular example, when working with AI coding assistants, SOLID principles become your quality control framework.
I've started using them as a checklist for reviewing AI-generated code:
Is this class doing too much? (SRP)
Will we need to modify this code when requirements change, or can we extend it? (OCP)
Can I swap implementations without breaking clients? (LSP)
Are these interfaces bloated with methods my components don't need? (ISP)
Am I depending on concrete details instead of abstractions? (DIP)
The best prompt engineers (yes, that's what we're becoming) aren't just asking for code – they're embedding architectural guidance into their requests.
The prompt "Generate a modular service layer for user account management that implements dependency injection, separates business logic from persistence, adheres strictly to the Dependency Inversion Principle, and includes interfaces allowing for easy replacement of the data access layer with both SQL and NoSQL implementations" gets you much better results than "create service for users please."
The robots aren't architects yet. They're sophisticated autocomplete. They still struggle with context. They don’t anticipate edge cases unless you explicitly tell them. They won’t naturally think about transaction boundaries, error handling strategies, or cross-cutting concerns like logging and security – unless you bake that into your prompt.
That’s it for today’s refresher on why fundamentals still matter.
I hope this episode has given you some food for thought about how your hard-earned knowledge remains valuable even when AI-vibes reshape our daily work. The tools may change, but the wisdom behind good software design remains constant.
Drop me a line with your experiences, keep your principles SOLID and your coding vibes even stronger! See you next time!
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