Ibrahim Sowunmi

Why C++ Gives LLMs a Headache

When a dev complains that a language is “hard,” they’re almost never talking about the parser. They’re talking about how many separate things they have to keep in their head at once. The same rule applies to large language models. LLMs—fancy next‑token predictors—are great at following a single, clean thread of thought. They stumble when the thread frays into an array of tiny filaments to be ran in parallel.

That, in a sentence, is why C++ isn’t as performant for LLM‑based code generation.

The Complexity Tax

If you squint, most programming languages are two products: the syntax and the ecosystem. C++ ships both at maximal power. The syntax includes templates, multiple inheritance, five kinds of cast, three kinds of initialization, and a memory model that still surprises veteran humans. The ecosystem adds a Cambrian explosion of tooling, from build systems to package managers: Make, CMake, Bazel, vcpkg, Conan… it’s a lot.

Humans cope by learning subsets. “We don’t use dynamic_cast here,” someone says, and suddenly the local dialect is that little bit closer to being understood. But an LLM fed the whole internet doesn’t get to ignore anything. It has to juggle all the dialects at once: Boost‑heavy modern C++, Qt‑era C++, 1998‑style pointer soup.

Multiplicity turns prediction into roulette. Ask the model for a vector class. Does it reach for std::vector, absl::InlinedVector, or a hand‑rolled small‑buffer‑optimized version copied from a 2004 blog post? The corpus won’t tell it; every style has thousands of examples.

Contrast this with a simpler language like Go. It has one official formatter, one dependency manager, one canonical error‑handling pattern, and until recently, zero generics. An LLM writing Go is basically dictating pseudocode. This is much more LLM‑tolerant.

What Makes C++ Hard for LLMs

State Explosion — A single snippet can depend on compiler flags, C++ standard version, platform quirks, and whether -fno‑exceptions is secretly enabled. The model has to recall that context, or bring it into context.

Templates Everywhere — Even “simple” templates double the branching factor. Should the function instantiate for int or std::string, or both? The model must predict every possible substitution path and its compile‑time consequences, this is far harder than using a concrete type.

Memory Ownership Semantics — unique_ptr vs. shared_ptr vs. raw pointers vs. std::span vs. custom arenas. Each choice changes the rest of the program. The model can’t know which one your team favors unless you tell it.

Build‑System Divergence — Even if the generated code is correct, wiring it into your build is another problem altogether. The model can’t guess the path to your .so, or whether you insist on Unity builds.

Notice the pattern: ambiguity everywhere. LLMs hate ambiguity because their job is to guess the next token. The more possible continuations, the flatter their probability distribution, and the more likely they are to pick garbage.

Windsurf: Shrinking the Search Space

The trick isn’t to tame C++ itself; that ship sailed with Bjarne in 1983. The trick is to narrow what the LLM has to consider and intelligently bring relevant pieces into context. Windsurf gives you four knobs for that:

1. Rules / Memories

Think of Memories as the house style guide the model can actually read. Drop in snippets that show your way of doing things—RAII wrappers, how you pass strings, which smart pointer wins. Suddenly the model’s probability mass shifts toward those patterns. Less roulette, more convergence.

https://windsurf.com/editor/directory

2. Workflows

LLMs forget step‑by‑step instructions faster than you can say “context window.” A Workflow is a saved script that reenacts a ritual: run Clang‑Tidy, apply the moral equivalent of gofmt (clang‑format), pipe the diff back as a patch. The model invokes the workflow instead of reinventing it. You get repeatability, and the model skips thirty tokens of fumbling.

https://docs.windsurf.com/windsurf/cascade/workflows

3. Intelligent Context Targeting (@‑mentions + MCP)

Ever wish you could tap the model on the shoulder and whisper, “Look here, not everywhere”? That’s what @‑mentions do. Prefix a symbol—@RingBuffer, @allocator_stats—and Windsurf increases its awareness of the code that defines or references it. Under the hood, MCP (Modular Context Processing) resolves those mentions, querying a Windsurf server that hosts your codebase in searchable shards. You can point MCP at specific enums, libraries, or even generated chunks. The LLM’s context window fills with exactly the headers and implementation bits it needs—no more, no less. Fewer tokens in, fewer wrong guesses out.

4. Planning Mode

Paul Graham once said good design is “omitting everything that’s obviously unnecessary.” Planning Mode forces the model to write that omission list first. It outlines intent—“allocate from arena, never heap”—before diving into the for‑loops. The outline becomes a checklist the generated code must satisfy. It’s the difference between drawing a map and wandering with a compass.

Putting It Together

Imagine asking the LLM to add a lock‑free queue. Raw C++ GPT‑style, you might get something that compiles only on GCC 13 with obscure flags. With Windsurf:

  • Planning Mode drafts the algorithm and notes the memory‑ordering constraints.
  • The draft references your Rules/Memories: a house RingBuffer template and a CacheAligned attribute macro.
  • @‑mentions in the prompt—@atomic_queue, @cpu_relax—trigger Intelligent Context Targeting, and MCP pulls only the headers for atomic wrappers and platform shims.
  • A Workflow runs clang‑format, your unit‑test harness, and static analysis, kicking back compile errors the model fixes in a loop.

The search space collapses from “all C++ ever written” to “the dozen ways you approve.” That’s enough to tip the odds back in our favor.

The Up‑Shot

C++ isn’t about to get simpler. But we can insulate our LLMs from its complexity by giving them fences. Windsurf’s Rules/Memories, Workflows, Intelligent Context Targeting, and Planning Mode are four sturdy pickets. Put them up, and even a probabilistic parrot can sound like a seasoned C++ engineer.

And who knows? Once the model stops stumbling over build files, we might remember why we liked C++ in the first place: it lets us get exactly what we want, as long as we can keep the whole program in our heads. With Windsurf, the model helps keep it there.