DRY, KISS, YAGNI in the LLM Era: What Still Holds, What Breaks

The classic principles of software engineering — DRY, KISS, YAGNI — were written for humans reading and writing every line of code. The constants of that world were obvious: typing is slow, reading is slower, and the cost of fixing a mistake compounds with every developer who touches the wrong abstraction.

LLM-assisted development changes those constants. Generating code is fast. Repeating yourself is no longer a typing problem. Some of the principles still hold; some quietly invert. Here is the 2026 version of each.

DRY — Don't Repeat Yourself

The original argument was straightforward: every concept should have a single, authoritative representation. If you duplicate the formula total = subtotal + tax, then when the tax rules change you will update it in three places, miss one, and ship a bug.

The principle is still correct for concepts. It is widely misapplied to code shapes. Two functions that look the same today but encode different business rules — invoice tax versus refund tax — are not duplication, they are similarity. Forcing them to share an implementation creates the wrong abstraction, and the wrong abstraction is more expensive than the duplication ever was.

What changed for LLMs. A model reading a codebase has to follow every layer of indirection in its head. A function that calls applyTaxRules(ctx, line) that dispatches on ctx.kind to one of three private helpers is harder for the model to reason about than three explicit applyInvoiceTax, applyRefundTax, applyCreditNoteTax functions side by side.

Heavy DRY abstractions — generic helpers with five parameters that "work for everything" — measurably hurt LLM code quality. The model has to load context from multiple files to understand what the helper does, and the more it has to load, the more wrong it gets.

Updated rule for 2026:

• DRY the concept, not the shape. If two pieces of code change for different reasons, they are not duplicates.
• Prefer three explicit, named functions over one parameterized abstraction, until you have enough use cases (three is the usual threshold) to know what the right abstraction is.
• Inline code that is only used in one place. Stop creating single-use private helpers.

KISS — Keep It Simple, Stupid

KISS held up best of the three. It says: prefer the simplest solution that solves the problem. The principle is even more correct in 2026, because LLMs are excellent at simple, idiomatic code and progressively worse at clever code.

The trap is that "simple" is contested. Some teams think "simple" means "shortest." Others think it means "fewest dependencies." Others think it means "what the team already knows."

The 2026 version: simple means "what the average engineer on this team can read and confidently modify after six months away." Not the cleverest. Not the most elegant. Not the most theoretically correct. The most boring code that does the job is almost always the right answer.

What changed for LLMs. Boring code is what LLMs are best at. Boring code is what they generate by default. Boring code is what they read most reliably. Every time you reach for a clever pattern — a generic with seven type parameters, a metaclass, a runtime-evaluated DSL — you are betting that the value of the cleverness exceeds the cost the model (and your team) will pay in comprehension.

That bet rarely pays. KISS in 2026 is "let the model give you the boring answer, and only override it if you have a specific reason."

YAGNI — You Aren't Gonna Need It

YAGNI says: do not build for hypothetical future requirements. Build for the requirements you have now. When the future arrives, build for it then.

This principle is more important in 2026, for an unintuitive reason. Writing code is cheap. Adding features speculatively is cheaper than ever. Which means the marginal cost of building something you might not need has dropped — and the temptation to do it has gone up correspondingly.

The trap: speculative features add complexity to the codebase that every future change has to navigate around. Every "we might want this" feature flag, every "in case we ever need to" extension point, every "future-proofing" abstraction is a cost that compounds.

Updated rule for 2026:

• The fact that something is easy to build is not a reason to build it.
• Feature flags are not free. Each one is a branch in the codebase that every future change has to consider.
• "We might internationalize this later" is the most expensive sentence in modern web development. Either internationalize now or do not — do not leave i18n hooks dangling unused.
• Extension points without a current consumer are dead code. Delete them.

YAGNI is harder to follow when the marginal cost of adding code is low. Discipline matters more than it used to.

Two principles worth adding

DRY/KISS/YAGNI were the principles of a smaller era. Two more belong on the list now.

LOL — Locality of Logic

If a function modifies a thing, the modification should be visible at the call site or close to it. Side effects buried three layers deep — a logger that writes to disk, a "service" that secretly enqueues a job, a hook that mutates global state — are the bugs LLMs are worst at finding because the cause and the effect are not in the same context.

A 2026 codebase should be readable top-to-bottom in any given file. If you have to chase a side effect across the call graph to understand what a function does, the architecture is failing the model and the human.

POLA — Principle of Least Astonishment, but for AI

This is POLA from the 1970s with a modern twist. Code should not surprise a reader. In 2026, the relevant reader is increasingly an LLM. Patterns that surprise the model — magic strings as dispatch keys, runtime monkey-patching, exception-as-control-flow, implicit globals — produce confident-but-wrong AI output.

When the model returns code that looks reasonable but does the wrong thing, ask yourself whether there is something astonishing in the codebase that misled it. Usually there is. Removing that surprise — even if it means slightly more verbose code — pays off the next ten times an agent touches that area.

When the classics still apply unchanged

It is worth being clear about where DRY/KISS/YAGNI are still exactly right:

• DRY for genuinely shared concepts — your Money type, your User model, your auth middleware. One source of truth, always.
• KISS for everything you will not rewrite — boring code that nobody has to think about is the highest-leverage code in the building.
• YAGNI for premature features — do not build the admin dashboard before you know who the admins are.

The principles did not become wrong. The application of them has narrowed, and a few new ones earn their keep alongside.

What this looks like in practice

The principles cash out as a different set of code review questions:

• Is this abstraction earning its keep, or is it the same shape being shared by two different concepts? (DRY)
• Is this the boring version, or did we get clever? Is the cleverness paying for itself? (KISS)
• Is this feature flag actually toggled in some environment, or is it speculative? (YAGNI)
• Can I see what this function does without leaving the file? (LOL)
• Will this surprise a fresh reader (human or model) six months from now? (POLA)

A team that internalizes these five questions writes a codebase that AI tools can extend rapidly and humans can maintain for years. The two goals turn out to be the same goal.

The principles were never about typing speed. They were about the cost of understanding — and in 2026, understanding is the only cost that matters.