Advanced Installation, Configuration, and Customization

In our last lesson, we established the core philosophy of Cursor as an AI-first editor. Now, we will translate that philosophy into a practical setup tailored for an engineering workflow. A powerful tool is only as effective as its configuration. For a developer, an editor is a highly personalized environment, honed over years to maximize efficiency and comfort.

This lesson focuses on moving beyond the default setup to create a Cursor environment that is truly our own. We will cover how to seamlessly migrate an existing VS Code setup, how to make critical decisions about performance and privacy, and how to customize the AI’s behavior to align with our specific project needs and team standards.

Seamless migration from VS code

One of Cursor’s most practical advantages is that it is a fork of VS Code. This shared foundation makes the transition from a traditional VS Code environment nearly frictionless. For developers who have invested years in customizing their setup, this is a critical feature.

When we first launch Cursor, or by navigating to Settings > General > VS Code Import, we have the option to import our existing configuration.

This process transfers:

• Settings: All of our settings.json configurations, from theme choices and font rendering, to file associations and indentation rules.

• Extensions: The extensions we rely on daily, such as language-specific linters (e.g., ESLint), formatters (e.g., Prettier), Git tools (e.g., GitLens), and framework-specific helpers.

• Keybindings: The custom keyboard shortcuts that are deeply ingrained in our muscle memory.

By importing our setup, we can start using Cursor’s advanced AI capabilities without sacrificing the personalized environment we have already perfected. This eliminates the steep learning curve often associated with adopting a new primary editor.

Configuring for performance and privacy

When building production-grade software, performance and privacy are not afterthoughts; they are primary requirements. Cursor provides granular control over both.

Model selection and performance trade-offs

The choice of AI model directly impacts the performance, quality, and cost of our interactions. In Settings > Models, we can select which models are available to us.

The decision involves a trade-off:

• High-performance models (e.g., GPT-4, Claude 3 Opus): These models provide advanced reasoning, leading to higher-quality code generation, more insightful refactors, and better architectural suggestions. However, they are slower to respond and more expensive to run (consuming more “requests” on a paid plan or costing more via API). We should use these for complex tasks like planning a new feature or refactoring a large module.

• High-speed models (e.g., GPT-4o mini, Claude 3.5 Sonnet, Gemini Flash): These models are optimized for low latency. They provide near-instantaneous responses, which is ideal for tasks like code completion, writing docstrings, or answering simple questions. They are less expensive but may not have the same depth of reasoning as the larger models. We should use these for our day-to-day, line-by-line coding assistance.

An effective workflow often involves dynamically switching between these models based on the task at hand. We can use a fast model for general coding, and switch to a more powerful one when we need deeper strategic assistance.

Data retention and privacy mode

Data privacy is a critical concern, especially when working with proprietary or sensitive code. In Settings > General > Privacy mode, we can configure how Cursor handles our data.

When Privacy Mode is enabled, Cursor operates with a zero-data-retention policy for our code. This means:

• No code from our editor is stored on Cursor’s servers.

• The context sent to third-party LLMs (like OpenAI or Anthropic) is used only to generate a response and is not retained or used for model training by those providers.

For enterprise environments, this mode can often be enforced at the organizational level, ensuring compliance with the company’s data policies. It is crucial to enable this setting when working on any non-public project.

Customizing AI behavior with rules

To make the AI a true partner, we need to teach it our preferences and project conventions. Cursor’s “Rules” provide a powerful mechanism for this, allowing us to provide persistent instructions to the AI.

• User rules: Found in Settings > Rules & Memories, these are global instructions that apply to all our projects. They help align the AI with our personal coding style. Examples include “Always use functional components in React” or “When writing Python, add type hints to all function signatures.”.

• Project rules: For project-specific conventions, we can create rule files inside a .cursor/rules directory at the root of our project. For example, a rule file named projectrules.mdc for a React project might contain:

By setting clear rules, we reduce the need to repeat instructions in our prompts and ensure that the AI’s output is more consistent and aligned with our standards from the start.

Scoping and organizing project rules

Once a project rule file is created, we have powerful options to control when it is applied. Each project rule file can be assigned a specific scope, giving us fine-grained control over when the AI uses its instructions.

We can choose from several application scopes as shown in the screenshot below:

• Always Apply: This rule is included in every chat and command-k session. It’s best for fundamental standards, like the project’s primary programming language or core architectural patterns.

• Apply Intelligently: This allows the Cursor agent to decide when the rule is relevant based on the task description. This is useful for more specific rule sets that aren’t needed for every single prompt, such as a guide to your component library. The agent will include it only when it determines the task involves that library.

• Apply to Specific Files: This scopes a rule to files matching a specific pattern (e.g., src/components/**/*.tsx). This is extremely powerful for applying different standards to different parts of a codebase, like the frontend and backend.

• Apply Manually: This is the most controlled option. The rule will only be included in the context if we explicitly mention it in our prompt using an @ reference (e.g., @projectrules). This is ideal for utility rules, style guides, or templates that we only need to reference occasionally.

In addition to these explicit scopes, Cursor supports nested rules. We can organize rules by placing them in .cursor/rules directories throughout our project. These nested rules automatically attach when files in their directory are referenced, which is perfect for monorepos with distinct front-end and back-end sections.

Consider the following project structure:

In this example:

• Rules in project/.cursor/rules/ will apply globally across the entire project.

• Rules in project/backend/.cursor/rules/ will automatically be included in the context whenever we are working with or referencing files within the backend/ directory.

• Similarly, rules in project/frontend/.cursor/rules/ will only apply to work done within the frontend/ directory.

This system allows for a highly organized and intuitive way to manage context, ensuring that the AI always has the most relevant instructions for the specific part of the codebase we are working on. This is achieved without a manual configuration of file patterns.

Setting up custom AI models and API keys

While Cursor provides built-in access to several models, an advanced workflow may require connecting our own API keys. We can do this in Settings > Models.

There are several reasons for this:

• Access to the latest models: LLM providers often release new models that may not yet be integrated into Cursor’s default list. Using our own API key grants us immediate access.

• Cost management and billing: For teams or individuals with existing accounts with OpenAI, Anthropic, or Google, using a personal API key allows for centralized billing and potentially lower costs, depending on usage patterns.

• Access to private deployments: Enterprises using services like Azure OpenAI or Amazon Bedrock can configure Cursor to route requests to their private, fine-tuned models, ensuring data never leaves their cloud environment.

To set this up, we simply input the API key from our chosen provider (e.g., OpenAI, Anthropic, Google) into the corresponding field in the settings. For services like Azure, we also need to provide the specific deployment name and endpoint URL.

The configuration files

While the Settings UI is the primary way to configure Cursor, it is helpful to know where these settings are stored.

• ~/cursor/user/settings.json: This file in our home directory stores most of our user-level configurations, similar to VS Code’s settings.json. We can edit this file directly for advanced tweaks.

• .cursor/rules: As mentioned, this file (located within a project’s root directory) is where we define project-specific rules for the AI. Committing this file to version control promotes consistency across the team by establishing shared conventions.

Understanding these files allows for direct manipulation of settings, and for sharing configurations across a team in a programmatic way.

Conclusion

We have now moved beyond the default Cursor experience. By migrating our trusted VS Code environment, we have created a familiar foundation. By carefully selecting our AI models and configuring privacy settings, we have made the tool both performant and secure. Moreover, by defining custom rules, we have begun to train the AI to be a true partner that understands our unique style and standards.

In our next lesson, we will build on this customized foundation to explore how Cursor’s deep-codebase understanding is achieved through indexing and how we can leverage it with @-references to provide the AI with the perfect context for any task.