Software Principles That Still Matter in the Vibe Coding Era

Vibe Coding. Some people call it "wish-driven development" — you make a wish, AI implements it. Sounds great in theory. In practice, opinions are split hard.

I've been using AI in my workflow since 2023, back when Claude Sonnet was still on version 3.5. A lot of people were skeptical, but even then, it was already handling significant chunks of my work. By now, most of the doubt has faded — honestly, a lot of the early negative takes came from people using the tools wrong.

What I want to share here is: even in a vibe coding workflow, some of those classic software principles still hold. You can't afford to throw them all out. For more on vibe coding vs traditional development, read this piece.

Clean Architecture — Still Applies

Before vibe coding was a term, software engineering had a whole library of approaches to managing complexity: Clean Architecture, The Mythical Man-Month, Domain-Driven Design — each a classic for a reason.

Clean Architecture's core idea — separate your business logic from frameworks, from UIs, from databases — is arguably more important when AI writes your code. AI generates what you ask for. If you don't enforce the boundary between "business logic" and "framework glue," the AI will happily merge them, and six months later you're locked into a codebase where every layer bleeds into every other layer. That's not the AI's fault — it wrote what you told it to. The architecture is your job.

entities → use cases → interfaces → frameworks. This arrow still works with AI. Define the core domain in clear, constrained prompts. Let AI fill in the implementations. Review the boundaries.

Entropy — LLMs Accelerate It

Software entropy — the tendency of a codebase to become messier over time — is real, and LLMs accelerate it if you're not paying attention.

Why? Because AI is an optimization machine. It optimizes for "solve the immediate problem in front of me." It doesn't feel the drag of accumulated tech debt the way you do after living in a codebase for months. If every prompt is "add feature X" without "and refactor Y to make room for it," the codebase drifts. Fast.

The fix isn't to stop using AI. It's to treat refactoring as a first-class prompt category. After every few feature additions, ask the AI: "Given the current state, what should be extracted? What's duplicated? What's getting brittle?" It'll spot things you've stopped noticing. You still need to approve the changes — but the detection cost drops to zero.

Context Management — Your Job, Not the AI's

The idea that "AI handles the details, so I don't need to keep context" is a trap.

When you're deep in a project, you carry a mental model of the codebase — the conventions, the edge cases, the decisions that were made three sprints ago. AI doesn't carry that. It reconstructs context from whatever you paste into the prompt window. If your prompt is lazy, its output is disconnected.

This means context management becomes a deliberate skill. Keep a project overview doc updated. Maintain a decisions log. When you hand off a task to AI, include the relevant constraints, not just the ticket description. The quality of AI output scales directly with the quality of context you feed it. Garbage context in, disconnected code out.

When to Say No to AI

AI will happily generate anything you ask for. That's both its strength and its danger.

Some things AI shouldn't touch unless you fully understand what it produced: authentication flows, cryptographic primitives, anything involving PII or compliance boundaries. If you can't review it competently, don't ship it — no matter how confident the AI sounds. This isn't an AI principle. It's always been true for library code you copy-paste from Stack Overflow. The tool is faster now, but the responsibility hasn't shifted.

Also: AI is bad at knowing when a feature shouldn't exist. It'll build whatever you describe. The judgment call — "should we even do this?" — is 100% yours.

Vibe coding is a legitimate productivity multiplier, but it doesn't replace engineering judgment. Clean Architecture still separates concerns. Entropy still accumulates. Context still lives in your head, not the model's. The principles that mattered before AI still matter — they just matter faster now.