A 65-line Markdown file named CLAUDE.md, inspired by Andrej Karpathy's criticisms of Large Language Model (LLM) code generation, has achieved widespread popularity on GitHub, reportedly making Claude Code's output "much smarter." This file, part of the forrestchang/andrej-karpathy-skills repository, gained over 400 stars in a single day, nearing 4,000 total, and has been ported into VS Code and Cursor extensions for easier application.

The core methodology of CLAUDE.md comprises four guiding principles, designed to be injected into the LLM's prompt to steer its behavior:

1. Think Before Coding: This principle instructs the LLM to perform a pre-computation phase. It requires explicit articulation of assumptions, querying for clarification when uncertainty arises, and pausing the generation process if confusion persists. This aims to mitigate LLMs' tendency to make unwarranted inferences or generate irrelevant code by ensuring a robust problem understanding stage.

2. Simplicity First: This directive mandates strict adherence to the prompt, prohibiting the generation of unrequested features, unnecessary abstractions, or superfluous error handling mechanisms. The goal is to constrain the LLM to minimalistic, direct code solutions, avoiding "hallucinated" or over-engineered outputs.

3. Surgical Changes: This principle emphasizes precise and localized modifications. The LLM is instructed to alter only the specified components of the codebase, leaving unrelated sections untouched. This encourages targeted interventions, minimizing the risk of introducing unintended side effects or widespread, unrequested refactoring.

4. Goal-Driven Execution: This guideline transforms abstract objectives (e.g., "add feature") into concrete, verifiable goals (e.g., "pass all unit tests"). This encourages the LLM to focus on measurable outcomes and implies an iterative generation process guided by clear success criteria, promoting a more deterministic and test-oriented approach to code development.

Users report that applying these principles significantly improves Claude's code generation, leading to reduced creativity in unintended areas, fewer unwarranted assumptions, decreased unnecessary refactoring, and the production of more stable and predictable code. While some, like Michiel Beijen (the original blogger who created the extensions), acknowledge the non-deterministic nature of LLM outputs makes absolute confirmation difficult, the widespread positive user feedback suggests a palpable improvement. This phenomenon highlights the profound impact of context engineering and prompt hacking, demonstrating how a simple textual intervention can noticeably enhance the performance of advanced, multi-billion-parameter LLMs, effectively addressing persistent issues like over-assumption and lack of clarification noted by Karpathy.