Using AI coding assistants
You can use large language models (LLMs) to accelerate your development with Modular by providing structured context about Modular Platform’s docs and code to your projects . We provide two mechanisms:
llms.txtfiles for broad documentation access..cursorulesfiles for specific coding guidelines.
Supply documentation to LLMs with llms.txt
Modular supports the llms.txt proposed standard, enabling LLMs to access our documentation at inference time. This allows LLMs the most up-to-date documentation providing more accurate and context-aware responses.
Modular provides the following llms.txt files:
- llms.txt: Contains an index of links with brief content descriptions for LLMs to navigate to detailed information.
- llms-full.txt: Provides all detailed content in a single file, removing the need for navigation.
- llms-mojo.txt: Includes documentation for the Mojo standard library, MAX AI Kernels, and MAX library.
- llms-python.txt: Contains MAX Python APIs documentation.
Integrate llms.txt with AI-assisted IDEs
You can leverage llms.txt files with IDEs that support tool calling, such as
Cursor or Windsurf, to
provide context directly within your development environment.
For example, when writing Mojo code, you can reference the llms-mojo.txt file
by using @docs.modular.com/llms-mojo.txt in your chat window. Your IDE will
then use this documentation to inform its suggestions, completions, and error
corrections.
Enhance LLM guidance with .cursorules
.cursorules, also known as project
rules, are a powerful way to give LLMs consistent reusable information. These
rules are usually stored in a .cursor/rules directory right within your
project, so they can be version-controlled and specifically scoped to your
codebase.
You can use Modular's .cursorules to assist in coding tasks or working with
Modular based projects:
-
general_behavior_rules.mdc: General rules for code creation. Emphasizes simplicity, thorough investigation, using existing solutions, descriptive naming, environment variables for configuration, robust error handling, documentation, assertions, virtual environments, and workspace-relative operations. -
git.mdc: Outlines best practices for using Git effectively. Includes guidance on code organization, commit strategies, branching models, and collaborative workflows. -
mojo.mdc: Enforces Mojo coding standards, performance optimizations, and best practices. Aims to ensure efficient and maintainable GPU-accelerated code, with guidance on code organization, memory management, and error handling.
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