A prediction from a few years ago aged badly: “AI will write the code, so studying design patterns is obsolete.” The first half came true — a large share of the diffs I review today were generated, not typed. The second half inverted. Now that engineers write less of the code themselves, the ability to recognize structure at a glance — to say “this is a Strategy,” “this wants to be a Decorator,” “this is Singleton hiding in a static class” — has quietly become the highest-leverage skill in the review chain.
Here’s the argument, from someone who teaches patterns and ships with AI tools daily.
Why does AI-generated code make patterns more important, not less?
Because review replaced writing as the bottleneck, and patterns are the vocabulary of review. When you wrote code yourself, structural understanding came for free — you’d made every decision. A generated diff arrives decision-free: plausible, compiling, and structurally unexamined. The reviewer who can name what they’re looking at evaluates a 400-line diff in minutes (“Factory, fine; but this retry logic wants to be a Decorator, not copy-paste in six handlers”). The reviewer who can’t reads line by line — or worse, skims and approves.
Naming is compression. “This should be Strategy” replaces twenty minutes of explaining why a switch statement over behavior types will hurt — the exact conversation I keep having in reviews of generated code, because models reproduce the most common solution in their training data, and the most common solution is a pile of ifs.
Don’t the models already know the patterns?
They know the patterns the way a library knows its books. Ask for a Strategy implementation and you’ll get a textbook one. But the hard part of patterns was never implementation — it’s selection: knowing that this tangle of conditionals is three strategies wearing a trench coat, or that the “flexible” abstract factory the model produced is speculative generality for a problem with exactly one variant. Models make the selection error in both directions: under-patterned duplication when the prompt is narrow, and over-patterned ceremony when the prompt says “make it extensible.”
Selection requires knowing the forces a pattern resolves and the cost it charges — which is precisely the part the patterns course spends its time on: not “here’s the UML,” but “here’s the smell, here’s the moment the pattern earns its complexity, here’s the moment it doesn’t.” That judgment is what you’re renting out when you review a generated diff.
Which patterns pay off most in AI-assisted codebases?
The composition patterns — Strategy, Decorator, Adapter, Facade — because they’re the ones that keep repeated generation from becoming repeated duplication. A model asked for “add logging to these six handlers” will happily paste the same try/log/rethrow into six places; a reviewer with Decorator in their vocabulary turns that into one wrapper. Adapter and Facade earn their keep at AI-system boundaries specifically: tool interfaces for agents are Adapters by construction, and every sane LLM integration hides the provider behind a Facade so the vendor can change without the codebase noticing.
Then there’s the observer-of-the-year: Chain of Responsibility, reborn as middleware in every agent framework. If you can see the classic structure inside the fashionable wrapper, framework churn stops being relearning and becomes recognition — the same reason C# engineers with pattern fluency picked up agentic coding workflows faster than anyone expected: hooks, sub-agents, and pipelines are patterns they already had names for.
Concretely, this is the lookup table I wish every reviewer of generated code had taped to their monitor. The third column matters most: a named request is actionable, “this feels messy” is not.
| Pattern | The smell in generated code | What to say in review |
|---|---|---|
| Strategy | A switch or if-else chain over behaviour types that grows every sprint | “These branches are strategies — extract one per behaviour” |
| Decorator | The same try/log/retry block pasted into six handlers | “This is one wrapper, not six copies” |
| Adapter | Provider SDK types leaking into your domain code | “Put an adapter at the boundary; the domain shouldn’t know the vendor” |
| Facade | Callers orchestrating five subsystem calls in a specific order | “Hide that sequence behind one entry point” |
| Factory | Direct construction of concrete types scattered across call sites | “Centralise construction; callers shouldn’t be picking implementations” |
| Chain of Responsibility | One function doing auth, validation, logging and business logic in sequence | “This is middleware — one stage per concern” |
| Speculative generality (the anti-pattern) | An abstract factory and interface hierarchy serving exactly one implementation | “One variant doesn’t need a hierarchy — delete the abstraction until a second appears” |
How should you actually review generated code for structure?
Review in two passes with different questions. Pass one, structural: What pattern is this trying to be? Is there duplication that wants a name? Is there ceremony that wants deleting? You’re not reading lines yet — you’re reading shapes, and it takes minutes. Pass two, semantic: now read the lines that survived pass one. Most teams do only pass two, which is why generated duplication accumulates — line-by-line reading verifies each copy is individually correct and never notices there are six of them.
You can only review what you can name. Pattern literacy is not an architecture elective any more — it’s the reading skill that decides whether AI-assisted development compounds or accumulates.
Make the structural pass teachable by making it verbal: reviewers name the pattern (or the smell) in the review comment. “Extract Strategy here” is actionable and educational; “this feels messy” is neither. Teams that adopt the naming habit build a shared structural vocabulary within a quarter — and that vocabulary compounds, because the advanced C# course material on reflection, generics and async patterns all lands differently once the GoF floor is in place.
The inversion, stated plainly
We used to learn patterns to write better code, and the writing practiced the patterns. Now the writing is increasingly free, which means the practice is gone — and the judgment has to come from deliberate study, because review is where the judgment gets exercised. Pattern literacy in 2026 is not an architecture elective; it’s the reading skill that determines whether AI-assisted development compounds into a clean codebase or a beautifully-formatted landfill. Learn to name what you see. The models won’t do that part for you.
