ChatGPT: Language Features that Help LLMs

Yes. If you’re thinking about programming-language design specifically to make LLM-generated code more likely to be correct, there is a very interesting set of features. The key idea is:

Move as much of the correctness problem as possible from “the LLM has to remember to do it right” into “the language makes wrong programs difficult or impossible to express.”

Recent research supports this direction: type-constrained generation, for example, has been shown to substantially reduce compilation errors in LLM-generated TypeScript and improve functional correctness. (DOI)

Features I’d prioritize

Language feature	What it prevents	Why it helps an LLM
Strong static types	Wrong kinds of values	Compiler catches many mistakes
Type inference	Verbose annotations	Gives the LLM safety without making code cumbersome
Algebraic data types	Invalid states	Forces explicit representation of alternatives
Exhaustive pattern matching	Forgotten cases	Compiler tells the LLM what it forgot
Option/Maybe types	Null-pointer errors	Makes absence explicit
Result/error types	Ignored errors	Forces error handling
Immutable-by-default data	Accidental mutation	Reduces hidden state
Ownership/borrowing	Lifetime/aliasing bugs	Compiler handles difficult reasoning
Effect types	Hidden I/O, mutation, exceptions	Makes side effects visible
Refinement types	Invalid values	Compiler can enforce properties such as `x > 0`
Contracts/pre/postconditions	Violated assumptions	Converts requirements into checkable obligations
Units/types for quantities	Unit mismatches	Prevents e.g. meters + seconds
Restricted implicit conversions	Subtle coercion bugs	Makes transformations explicit
Simple, regular syntax	Syntax mistakes	Easier token prediction
One obvious idiom	API/design ambiguity	Reduces the number of plausible-but-wrong solutions
Deterministic semantics	Surprising behavior	Easier for the model to reason about
Compile-time verification	Logic errors	Gives the LLM immediate feedback

The really interesting ones are the last few layers.

1. Algebraic data types + exhaustive matching

Instead of:

status = 0   // maybe pending
status = 1   // maybe success
status = 2   // maybe failureCode language: JavaScript (javascript)

you have:

Status =
    | Pending
    | Success(Data)
    | Failure(Error)Code language: JavaScript (javascript)

Then:

match status:
    Pending       -> ...
    Success(data) -> ...
    Failure(err)  -> ...

If the LLM adds another variant, the compiler can point out every place that needs updating.

This creates a very useful feedback loop for an LLM:

model makes change → compiler identifies consequences → model fixes consequences.

That is much better than expecting the model to mentally discover every consequence.

2. Refinement types

This is even more powerful.

Instead of:

age: IntCode language: HTTP (http)

you could have:

age: Int where 0 <= age <= 150Code language: HTTP (http)

or:

NonZeroInt

or:

Index(array.length)Code language: CSS (css)

Now the language carries some of the meaning of the program, rather than merely its data representation.

Refinement types explicitly add logical predicates to ordinary types, allowing the type checker to enforce properties about values. (DOI)

This is particularly attractive for LLMs because it turns:

“Remember that this value must never be zero.”

into:

“The compiler won’t accept a value that violates this.”

3. Contracts

You can go another step:

fn divide(a: Int, b: Int) -> Int
    requires b != 0
    ensures result * b == a

Now the LLM doesn’t have to reason solely from comments and function names.

The interface itself describes the correctness conditions.

Some newer language experiments explicitly combine contracts, refinement types, ADTs, and compiler verification for this purpose. (GitHub)

4. Explicit effects

This is an underrated one.

Imagine:

fn calculate(x: Int) -> Int

versus:

fn calculate(x: Int) -> Int
    effects { IO, Database }

The second tells the LLM that calling this function isn’t just computation—it has externally visible consequences.

You could potentially distinguish:

pure
IO
network
filesystem
database
mutation

This makes action at a distance much harder.

Several newer AI-oriented languages are explicitly experimenting with this sort of design, including explicit effects, ownership, and algebraic errors. (GitHub)

The really interesting design principle

I would actually organize the language around a hierarchy:

Syntax
   ↓
Types
   ↓
Data invariants
   ↓
Resource invariants
   ↓
Effect invariants
   ↓
Behavioral contracts
   ↓
Tests / formal verification

At each level, the language gives the LLM more information and fewer ways to be wrong.

For example:

Int

is weak.

NonZeroInt

is stronger.

UserId

is stronger still.

ExistingUserId

is stronger.

And:

ExistingUserId
    requires user.existsCode language: CSS (css)

starts getting into actual behavioral verification.

There’s another feature I’d put unusually high on the list

Make the compiler’s error messages extremely machine-readable.

For an LLM, this:

error[E042]:
  expected: NonEmptyList<User>
  found: List<User>
  at: users[37]
  reason: list may be empty
  suggested fixes:
    - check for empty list
    - use `head?`
    - establish `length > 0`Code language: PHP (php)

is vastly more useful than:

Type mismatch.

In other words, the compiler becomes part of the LLM’s reasoning system.

That’s an important distinction. You don’t necessarily need a language where the LLM magically writes perfect code. You want a language where:

the LLM can cheaply propose code, and the language can cheaply tell it exactly what is wrong.

Research on constrained decoding is moving in essentially this direction: rather than merely asking an LLM to generate valid code probabilistically, the generation process itself can be constrained by the language’s formal rules. One 2025 study found that type-constrained decoding reduced compilation errors by more than half. (DOI)

If I were designing an “LLM-native” language

I’d probably combine:

Rust

ownership
borrowing
strong static types

Haskell/ML

algebraic data types
pattern matching
type inference

Idris/Liquid Haskell-style systems

dependent/refinement types
proofs

Effect systems

explicit side effects

Design by contract

preconditions/postconditions

LLM-specific additions

extremely regular syntax
minimal number of equivalent idioms
highly structured compiler errors
machine-readable AST
automatic test generation
incremental compilation
explicit interfaces
APIs whose types encode as much semantic information as possible

There are already projects explicitly exploring this premise—for example, Almide describes itself as a statically typed language optimized around LLM code generation, while Promise is designed around explicit ownership, effects, errors, and “one obvious way” to express things. (GitHub)

The deeper idea is that programming languages for LLMs may want to optimize not for human typing speed, but for the ratio of semantic information conveyed per token and incorrect programs rejected by the compiler.

That’s a potentially very different programming-language design target from conventional languages.

Yes. I’d expand the comparison to include the major mainstream languages plus several languages that are especially interesting from the “LLM-friendly language design” perspective. The 2025 Stack Overflow survey includes Python, JavaScript, SQL, Java, C++, C#, TypeScript, PHP, Rust, Go, Lua, Kotlin, Swift, Ruby, Dart, and many others among its surveyed languages. (Stack Overflow Developer Survey)

Here is the table in the format you requested. “Has it” means the language provides the feature in a meaningful built-in language/type-system form; “doesn’t” means it lacks it or requires a library/convention rather than the language enforcing it.

Language features	Language has it	Language doesn’t have it
Strong static typing	Rust, C++, C#, Java, TypeScript, Go, Kotlin, Swift, Elm, Haskell, F#, OCaml, Scala, Dart, Zig	Python, JavaScript, Lua, Ruby, PHP
Type inference	Rust, C++, C#, Java, TypeScript, Go, Kotlin, Swift, Elm, Haskell, F#, OCaml, Scala, Dart, Zig	C, PHP, Lua, JavaScript
Algebraic data types	Rust, Elm, Haskell, F#, OCaml, Scala, Swift, Kotlin	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Exhaustive pattern matching	Rust, Elm, Haskell, F#, OCaml, Scala, Swift, Kotlin	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Option/Maybe type	Rust, Elm, Haskell, F#, OCaml, Scala, Swift, Kotlin, Java, C#, TypeScript	C, C++, Go, Python, JavaScript, Lua, Ruby, PHP
Result/error type	Rust, Elm, Haskell, F#, OCaml, Swift, Scala	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Null safety enforced by type system	Rust, Elm, Kotlin, Swift, Dart, C#, TypeScript*	C, C++, Java, Go, Python, JavaScript, Lua, Ruby, PHP
Immutable-by-default	Elm, Haskell, F#, OCaml	Rust, C++, C#, Java, TypeScript, Go, Python, JavaScript, Lua, Ruby, PHP
Immutable data structures built into language	Elm, Haskell, Clojure	Rust, C, C++, Java, C#, Go, Python, JavaScript, Lua, Ruby, PHP
Ownership system	Rust	C, C++, C#, Java, TypeScript, Go, Python, JavaScript, Lua, Ruby, PHP
Borrow checker	Rust	C, C++, C#, Java, TypeScript, Go, Python, JavaScript, Lua, Ruby, PHP
Automatic memory management	Java, C#, Go, Python, JavaScript, TypeScript, Kotlin, Swift, Dart, Ruby, PHP, Lua, Haskell, Elm, Scala, Clojure	C, C++, Rust, Zig
Manual memory management	C, C++, Zig	Rust, Java, C#, Go, Python, JavaScript, TypeScript, Kotlin, Swift, Dart, Ruby, PHP, Lua, Elm, Haskell
Compile-time bounds/safety checking	Rust, Ada, SPARK	C, C++, C#, Java, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Refinement types	Liquid Haskell, F*, Idris, Ada/SPARK	C, C++, Rust, C#, Java, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP, Elm
Dependent types	Idris, Agda, Lean, Coq	C, C++, Rust, C#, Java, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP, Elm
Design-by-contract	Eiffel, Ada/SPARK, Dafny, Whiley	C, C++, Rust, C#, Java, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP, Elm
Formal verification facilities	Lean, Coq, Isabelle, F*, Dafny, SPARK, Idris	C, C++, Rust, C#, Java, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP, Elm
Compile-time assertions	C, C++, Rust, C#, D, Swift, Zig	Python, JavaScript, TypeScript, Lua, Ruby, PHP, Java, Go
Unit/dimension types	F#, Ada, Haskell libraries, Rust libraries, Scala libraries	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP, Elm
Explicit effects system	Haskell, Koka, Eff, Unison, OCaml (effect handlers)	C, C++, Rust, C#, Java, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP, Elm
Explicit exception/effect distinction	Haskell, Koka, Eff	C, C++, Java, C#, Python, JavaScript, TypeScript, Ruby, PHP, Go, Rust
Pure functions encouraged/enforced	Haskell, Elm	C, C++, Rust, C#, Java, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Referential transparency	Haskell, Elm	C, C++, Rust, C#, Java, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Restricted implicit conversions	Rust, Go, Ada, Haskell, Elm	C, C++, JavaScript, PHP, Python, Ruby
No implicit null values	Rust, Elm	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Exhaustive enum handling	Rust, Swift, Kotlin, Elm, Haskell, F#, OCaml	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Explicit visibility/access control	C++, C#, Java, Rust, Kotlin, Swift, Go, Python, TypeScript, Ruby, PHP	Lua, JavaScript
Structural typing	TypeScript, Go	C, C++, Rust, Java, C#, Kotlin, Swift
Nominal typing	Rust, C++, Java, C#, Kotlin, Swift, Go, Haskell, Elm	TypeScript, Python, JavaScript, Lua, Ruby
Interfaces/traits/typeclasses	Rust, Haskell, Go, Java, C#, TypeScript, Swift, Kotlin, Scala, F#	C, C++, Python, JavaScript, Lua, Ruby, PHP
Generics	C++, Rust, Java, C#, Go, TypeScript, Swift, Kotlin, Scala, Haskell, F#, Dart	C, Python, JavaScript, Lua, Ruby, PHP
Compile-time metaprogramming	C++, Rust, Haskell, Scala, Lisp/Clojure, Nim, Zig	Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Macros	C, C++, Rust, Lisp/Clojure, Elixir, Scala, Nim	Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Hygienic macros	Rust, Scheme/Racket, Clojure, Elixir	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Algebraic effects	Koka, Eff, Unison, OCaml	C, C++, Rust, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Actor/concurrency model	Erlang, Elixir, Pony, Scala/Akka	C, C++, Rust, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Lightweight concurrency primitives	Go, Erlang, Elixir, Kotlin, Rust, C#, Java	C, C++, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Compile-time race/data-race prevention	Rust	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Memory safety enforced by compiler	Rust, Ada/SPARK, Swift (in significant respects)	C, C++, Go, Java, C#, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Deterministic destruction/resource management	C++, Rust, Swift	Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
RAII	C++, Rust	C, Java, C#, Go, Python, JavaScript, TypeScript, Kotlin, Swift, Lua, Ruby, PHP
Linear/affine types	Rust, Haskell extensions, Clean, Linear Haskell	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Units of ownership/resources tracked by type system	Rust, Linear Haskell, Clean	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
Compiler-enforced initialization	Rust, C#, Java, Kotlin, Swift, Go	C, C++, Python, JavaScript, Lua, Ruby, PHP
Compiler-enforced unreachable-state detection	Rust, Elm, Haskell, F#, OCaml	C, C++, Java, C#, Go, Python, JavaScript, TypeScript, Lua, Ruby, PHP
First-class functions	Python, JavaScript, TypeScript, Rust, C++, C#, Java, Go, Kotlin, Swift, Elm, Haskell, F#, OCaml, Scala, Ruby, Lua, PHP	C
Lambda expressions	C++, Rust, C#, Java, Go, Python, JavaScript, TypeScript, Kotlin, Swift, Elm, Haskell, F#, OCaml, Scala, Ruby, Lua, PHP	C
Simple/regular syntax	Go, Elm, Lua, Python, Haskell	C++, C, Rust, Scala, C#, Java, JavaScript, PHP
Single obvious idiom encouraged by language	Go, Elm, Python	C++, C, JavaScript, PHP, Perl
Machine-readable AST/tooling	Most modern languages	—
Compiler diagnostics designed for automated tooling	Rust, Elm, Haskell, TypeScript, Swift, Kotlin, modern C#/Java	Older C/C++ toolchains and many dynamically typed languages
Static analysis ecosystem	Rust, C++, C#, Java, TypeScript, Go, Kotlin, Swift, Python	Lua, PHP, Ruby and others have varying degrees
Language-server/type-aware tooling	Rust, C++, C#, Java, TypeScript, Go, Kotlin, Swift, Python, Elm	Some smaller/older languages have less mature tooling

* TypeScript’s strictNullChecks can provide strong null checking, but JavaScript interoperability and escape hatches mean it isn’t equivalent to Rust/Elm’s guarantees.

The languages I’d include in the experiment

For what you’re investigating, I’d divide them into four groups:

Mainstream languages

Python
JavaScript
TypeScript
C
C++
Java
C#
Go
Rust
Swift
Kotlin
PHP
Ruby
Dart
Lua

Functional/type-system languages

Elm
Haskell
F#
OCaml
Scala
Clojure
Elixir
Erlang

Systems/newer languages

Zig
Nim
D
Crystal

Languages specifically interesting for extreme correctness

Ada/SPARK
Idris
Lean
Coq
F*
Dafny
Liquid Haskell
Koka
Pony

The distinction is important because the most popular languages aren’t necessarily the most useful ones for investigating LLM-oriented language design. For example, Rust is especially interesting because its ownership and borrow-checking system gives the compiler substantial ability to reject incorrect programs; Stack Overflow’s 2025 survey also reports Rust as the most admired programming language. (Stack Overflow Developer Survey)

If our ultimate goal is to answer “Which language features actually make LLM-generated programs more correct?”, I’d narrow the next table to perhaps 25–30 features and put each language as a column. That would produce a much more useful matrix:

Feature	Python	Rust	C++	TypeScript	JavaScript	Elm	Go	…
Static typing	✓	✓	✓	✓	—	✓	✓	…
Algebraic types	—	✓	—	—	—	✓	—	…
Exhaustive matching	—	✓	—	—	—	✓	—	…
Ownership	—	✓	—	—	—	—	—	…
Refinement types	—	—	—	—	—	—	—	…
Contracts	—	—	—	—	—	—	—	…
Effect system	—	—	—	—	—	—	—	…
…

That matrix would let us do something particularly interesting: look for combinations of features that give an LLM the strongest compiler feedback loop and therefore potentially make an LLM-native programming language substantially more reliable.

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