Mastering AI Code Assistants - Cursor Best Practices

Treat Cursor as a Pair Programmer

Engage with Cursor interactively. Provide context, review its suggestions, and iterate on the code together. Think of it as a collaborator that helps you write and refine code, not just a vending machine that dispenses code snippets. A key part of this collaboration is to carefully review AI's output. While you might write less code directly, you'll spend more time reading and validating AI-generated code, descriptions, and summaries to ensure they align with your goals.

Understand AI's Strengths and Limits

AI excels at well-defined, structured tasks like generating boilerplate, refactoring based on patterns, and implementing known algorithms. It may struggle with highly complex or novel problems, or navigating poorly organized and undocumented codebases without clear guidance. Recognize when to lead and when to let it assist.

Maintain a Well-Organized Codebase

A clean, organized codebase is crucial for Cursor to understand context and generate relevant, high-quality code. Focus on:

• Separation of Concerns: Ensure modules and components have distinct, well-defined responsibilities.
• Clear Naming Conventions: Use consistent and descriptive names for files, variables, functions, and classes. This helps the AI (and humans!) understand the code's purpose.
• Predictable File Structure: Organize files logically. A consistent structure allows Cursor to better infer relationships and locate relevant context.
• Strong Documentation: Write clear comments, docstrings, and READMEs. Use @ symbols in your prompts to refer Cursor to specific documentation files (e.g., @project_docs/architecture.md).

Define and Document Coding Patterns

Establish clear coding patterns early in your project and document them where Cursor can access this information. This helps AI generate code consistent with your existing architecture. Examples:

• Routes: Clearly define how API entry points are structured (e.g., @routes/user_routes.ts).
• Services: Use for orchestrating workflows, encapsulating business logic (e.g., @services/OrderService.java).
• Actions/Repositories: Isolate external interactions like API calls or database queries (e.g., @data/UserRepository.py).
• Shared Schemas and Types: Define data structures centrally (e.g., in @types/common.d.ts or @schemas/ProductSchema.json) and use them consistently. Reference these in prompts.

Plan Workflows Carefully Before Coding

Before diving into implementation, especially for new features or significant refactors:

• Define Scope: Clearly outline necessary types, function/method signatures, expected database interactions, and the intended location of new files or code blocks.
• Iterative Planning: Planning isn't a one-time step. Revisit and refine your plan as the project grows and requirements evolve. Break down large tasks into smaller, manageable steps.
• Co-Write Plans with AI: Use Cursor to help draft or refine your implementation plan. Ask it to outline steps, suggest alternative approaches, or identify potential challenges.
• Save for Reference: Keep these plans (e.g., in a scratchpad, a PLAN.md file, or chat history) to guide your coding process. Consider asking Cursor to help write this plan into a markdown file (e.g., feature-plan-X.md) that you can refer back to and even include in future prompts using @.
• Alignment is Key: Ensure the plan is fully clear and agreed upon (by you and your AI pair) before writing code. This minimizes rework and confusion.
• You are the Architect: Remember, you are the architect. AI can help you design and build, but the ultimate responsibility for the architecture and its soundness remains with you.

Anchor AI with Code Examples ("Show, Don't Just Tell")

Guide Cursor by providing concrete examples from your own codebase. Instead of a vague request, be specific: "Follow the pattern from @components/UserCard.jsx to create a ProductCard component displaying product name, image, and price." Using @ references to specific files or symbols is key here.

Adjust Task Granularity to Complexity

Tailor the scope of your AI requests to the task at hand: A key skill is learning where to 'put the cursor'—that is, how much to delegate to the AI at once. This often means starting broad for initial scaffolding and becoming more granular for complex details or refinements.

• Broad Scope (Agent Mode): For initial feature scaffolding, generating larger boilerplate, or multi-file changes. Example: "Create a new set of CRUD endpoints for resource X, including routes, services, and basic tests, using @template/crud_module as a reference."
• Narrow Scope (Inline or Chat): For implementing specific functions, methods, refactoring smaller code blocks, or when complexity increases. Example: "Refactor the calculateTotal method in @Order.java to include tax calculation based on @TaxService.getRate()."

Adapt Your Mindset: Collaborate, Don't Dictate

Working effectively with AI like Cursor requires a shift from simply "telling the computer what to do" to a more collaborative, iterative approach. It's not about working harder, but working *smarter* and *differently*. Embrace this new paradigm: focus on clear communication, strategic planning, and leveraging the AI's strengths to augment your own. This change in workflow is where the revolutionary productivity gains are found.