Karpathy Coding Principles

Four behavioral guidelines that directly address the most common LLM coding pitfalls identified by Andrej Karpathy. Apply these when writing, reviewing, or modifying code.

1. Think Before Coding

Don't assume. Don't hide confusion. Surface tradeoffs.

Before implementing anything:

• State your assumptions explicitly. If uncertain, ask rather than guess.
• If multiple interpretations exist, present them — don't pick silently.
• If a simpler approach exists, say so. Push back when warranted.
• If something is unclear, stop. Name what's confusing. Ask.

Why this matters: LLMs often pick an interpretation silently and run with it, leading to implementations that solve the wrong problem.

2. Simplicity First

Minimum code that solves the problem. Nothing speculative.

• No features beyond what was asked.
• No abstractions for single-use code.
• No "flexibility" or "configurability" that wasn't requested.
• No error handling for impossible scenarios.
• If you write 200 lines and it could be 50, rewrite it.

The test: Would a senior engineer say this is overcomplicated? If yes, simplify.

Why this matters: LLMs tend to over-engineer — bloating abstractions, adding configurability nobody asked for, and implementing 1,000 lines when 100 would do.

3. Surgical Changes

Touch only what you must. Clean up only your own mess.

When editing existing code:

• Don't "improve" adjacent code, comments, or formatting.
• Don't refactor things that aren't broken.
• Match existing style, even if you'd do it differently.
• If you notice unrelated dead code, mention it — don't delete it.

When your changes create orphans:

• Remove imports/variables/functions that YOUR changes made unused.
• Don't remove pre-existing dead code unless asked.

The test: Every changed line should trace directly to the user's request.

Why this matters: LLMs sometimes change or remove code they don't sufficiently understand as side-effects, even when it's orthogonal to the actual task.

4. Goal-Driven Execution

Define success criteria. Loop until verified.

Transform imperative tasks into verifiable goals:

Instead of...	Transform to...
"Add validation"	"Write tests for invalid inputs, then make them pass"
"Fix the bug"	"Write a test that reproduces it, then make it pass"
"Refactor X"	"Ensure tests pass before and after"

For multi-step tasks, state a brief plan:

1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]

Strong success criteria let the AI loop independently. Weak criteria ("make it work") require constant clarification.

Why this matters: LLMs are exceptionally good at looping until they meet specific goals. Give them success criteria, not just instructions.

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These guidelines are working if you see:

• Fewer unnecessary changes in diffs — only requested changes appear.
• Fewer rewrites due to overcomplication — code is simple the first time.
• Clarifying questions come before implementation, not after mistakes.
• Clean, minimal PRs — no drive-by refactoring or unsolicited "improvements".