Every few years, someone declares that the old principles of software engineering are obsolete. Object-oriented design is dead, they say. Microservices make SOLID irrelevant. AI can write code faster than humans can review it, so why bother with abstractions?
These claims are wrong. The principles that emerged from decades of hard-won experience in building and maintaining large software systems have not become less important. In fact, the explosion of AI-generated code in 2026 has made them more critical than ever. When code is cheap to produce but expensive to maintain, the engineering discipline you apply to it determines whether your codebase remains an asset or becomes a liability.
At Pepla, we train every developer on these fundamentals, not because we are traditionalists, but because they work. Let us walk through the principles that continue to separate professional software engineering from mere code production.
SOLID: Five Principles, One Goal
Robert C. Martin's SOLID principles were originally articulated for object-oriented design, but their underlying wisdom applies to any paradigm. They are about managing dependencies and controlling the impact of change.
SOLID principles manage dependencies and control the impact of change -- regardless of who writes the code.
Single Responsibility Principle (SRP)
A module should have one, and only one, reason to change. This does not mean a class should do only one thing (a common misunderstanding). It means a class should be responsible to only one actor, one stakeholder, one source of requirements.
Modern example: an AI-generated service class that handles user authentication, sends email notifications, and logs audit events. It works, but when the email provider changes, you are modifying the same module that handles authentication. When audit requirements change, you risk breaking login. SRP tells you to separate these concerns into distinct modules with clear boundaries.
Open/Closed Principle (OCP)
Software entities should be open for extension but closed for modification. You should be able to add new behaviour without changing existing code. In 2026, this is most commonly achieved through interfaces, strategy patterns, and plugin architectures.
Consider a payment processing system. Instead of a growing switch statement that handles Stripe, PayFast, Ozow, and every future payment provider, define a payment gateway interface and implement each provider as a separate class. Adding a new provider means adding a new file, not modifying existing ones.
Liskov Substitution Principle (LSP)
Subtypes must be substitutable for their base types without altering the correctness of the programme. This sounds academic until you encounter a violation. The classic example is a Square class that extends Rectangle and breaks when calling code sets width and height independently.
In modern TypeScript or C# codebases, LSP violations often manifest as API responses that nominally implement the same interface but have different null-handling behaviour or throw unexpected exceptions. Type systems help, but they cannot catch all violations. Careful contract design is still essential.
Interface Segregation Principle (ISP)
No client should be forced to depend on methods it does not use. Keep interfaces small and focused. A UserRepository interface that includes methods for CRUD operations, full-text search, analytics queries, and bulk imports forces every implementation to handle concerns it may not support.
Split it into UserReader, UserWriter, UserSearcher, and UserBulkOperations. Consumers depend only on what they need, and implementations can be composed from focused building blocks.
Dependency Inversion Principle (DIP)
High-level modules should not depend on low-level modules. Both should depend on abstractions. This is the foundation of testable, maintainable architecture. Your business logic should not know whether data comes from PostgreSQL, MongoDB, or an in-memory cache. It depends on a repository abstraction, and the concrete implementation is injected at runtime.
SOLID principles are not rules to follow blindly. They are design heuristics that help you make better decisions when the code gets complex. The skill is knowing when to apply them rigorously and when a simpler approach suffices.
DRY vs WET: Finding the Balance
Don't Repeat Yourself (DRY) is perhaps the most abused principle in software engineering. Taken to extremes, it produces deeply abstracted code that is harder to understand and modify than the duplication it eliminated.
The counter-principle, Write Everything Twice (WET), argues that you should tolerate duplication until you have at least three instances and a clear understanding of what they have in common. Premature abstraction, driven by DRY zealotry, creates tight coupling between unrelated features that happen to share some code today but may diverge tomorrow.
The pragmatic approach: duplicate freely when exploring, then refactor once patterns emerge. The "Rule of Three" is a useful heuristic: the first time you write something, just write it. The second time, note the similarity. The third time, extract the abstraction. By the third occurrence, you understand the commonality well enough to create a good abstraction.
AI-generated code needs SOLID principles more, not less -- machines do not intuit good design.
KISS: Keep It Simple
KISS (Keep It Simple, Stupid) is the principle that AI-generated code violates most frequently. Language models optimise for correctness and completeness, not simplicity. They will generate a factory pattern where a constructor would suffice, use generics where a concrete type is fine, and add error handling for conditions that cannot occur in your specific context.
Simplicity is not about writing fewer lines of code. It is about minimising cognitive load for the next person who reads it. Simple code is:
- Easy to read without extensive context
- Obvious in its intent
- Straightforward to debug
- Predictable in its behaviour
When reviewing AI-generated code, the most valuable question you can ask is: "Is there a simpler way to achieve this?" More often than not, the answer is yes.
YAGNI: You Aren't Gonna Need It
YAGNI is the antidote to speculative generalisation. Do not build features, abstractions, or infrastructure for requirements that do not exist yet. This is hard advice to follow because anticipating future needs feels responsible and professional. But the cost of unused abstractions is real: they add complexity, increase the learning curve for new team members, and often turn out to be wrong when the actual requirement eventually arrives.
The cost of adding something later, when you actually need it and understand the real requirements, is almost always lower than the cost of maintaining a premature abstraction that may not fit.
YAGNI saves you from building features nobody asked for -- resist speculative complexity.
Separation of Concerns
Each module, layer, or component should address a single concern. The presentation layer should not contain business logic. The data access layer should not format error messages for the UI. The authentication module should not know about billing.
This principle operates at every scale: within a function (separate computation from side effects), within a class (separate state management from business rules), within a service (separate API handling from domain logic), and within a system (separate authentication from business services).
In practice, separation of concerns manifests as:
- Layered architecture: Controllers, services, repositories, each with a clear responsibility
- Domain-driven design: Bounded contexts that isolate different areas of the business
- Event-driven patterns: Decoupling producers from consumers through asynchronous events
- Modular monoliths: Clear module boundaries within a single deployable unit
AI generates the raw material. Engineering principles shape it into something maintainable and testable.
Why AI-Generated Code Needs These Principles More, Not Less
AI coding assistants in 2026 are remarkably capable. They can generate working implementations of complex features in seconds. But they have a fundamental limitation: they optimise for the immediate prompt, not for the long-term maintainability of your codebase.
AI-generated code tends to:
- Duplicate logic rather than discovering existing abstractions in your codebase
- Over-engineer simple problems with unnecessary patterns
- Ignore the architectural conventions established in your project
- Create tight coupling between components that should be independent
- Mix concerns within a single function or class for the sake of "completeness"
This does not mean AI tools are bad. It means the developer's role has shifted from writing code to engineering systems. You use AI to generate the raw material, then apply engineering principles to shape it into something maintainable, testable, and aligned with your architecture.
The developers who thrive in 2026 are not the fastest typists or the most prolific coders. They are the ones who understand why code should be structured a certain way, and can apply that understanding whether the code was written by a human or a machine.
These principles have survived because they address fundamental challenges that do not change with technology: managing complexity, controlling the impact of change, and making systems that humans can understand, modify, and trust. As long as those challenges exist, these principles will remain relevant.
