Maximizing Your Cursor Use: Advanced Prompting, Cursor Rules, and Tooling Integration

For us developers working with Cursor, productivity isn’t just about writing code — it’s about optimizing how you interact with your AI assistant. In this article, we explore how to leverage advanced prompting techniques (including chain-of-thought and few-shot prompting), enforce best practices via Cursor rules, integrate powerful tooling and extensions, and incorporate rich external documentation into your workflow. We also share practical tips for managing long codebases while collaborating with AI assistants like Gemini and Claude. The result? An environment where AI not only assists but also learns, remembers, and orchestrates complex workflows to boost your productivity.

1. Leveraging Prompting Techniques

Chain-of-Thought Prompting

Chain-of-thought (CoT) prompting enables complex reasoning by guiding your AI through intermediate steps. By “nudging” the assistant to explicitly articulate its reasoning, you improve how it handles multi-part tasks. For example, a prompt for solving an arithmetic challenge might be structured like this:

Prompt:
The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1.
A: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False.

This step-by-step demonstration helps the model replicate similar reasoning in future tasks. Learn more about CoT on the Prompt Engineering Guide.

Few-Shot Prompting with Examples

Few-shot prompting provides one or more in-context examples to guide the model on how to complete a task. For instance:

Prompt:
A “whatpu” is a small, furry animal native to Tanzania. For example:
We were traveling in Africa and we saw these very cute whatpus.
To do a “farduddle” means to jump up and down really fast. An example sentence:
When we won the game, we all started to farduddle in celebration.

This approach ensures the model learns the expected format, especially when dealing with nuanced language tasks. For additional details, refer to the Few-Shot Prompting guide.

2. Best Practices with Cursor Rules

Plan and Act Modes

Maximizing Cursor’s efficiency starts with a clear plan before executing tasks. The concept of “Plan vs. Act” — discussed on the Cursor Community Forum — emphasizes that poorly planned instructions may lead to cascading failures. Always validate your plan before taking action, and if issues occur, log the failure in your .cursorrules file to refine your approach.

Logging Failures

Always track errors or undesired outcomes in your .cursorrules. This log helps the AI "learn" from past mistakes and improves the overall effectiveness of your prompts over time.

Versioning and Documentation

Document your changes and maintain separate branches for experiments. Trace back successful configurations or revert to previous versions when needed. Instruct your AI to generate summaries of key updates and integrate those into your .cursorrules. For a deep dive into managing these rules, visit the Cursor Rules repository.

3. Adding Full Repository Context, PDFs, and Other Docs

Integrating comprehensive external documentation — such as full repository contents, academic papers, or PDF guides — enhances your AI’s context for better-informed suggestions. Based on the Cursor Forum tutorial, here are the key steps:

Adding PDFs to Cursor

1. Convert the PDF:
   Use a tool like Marker to convert your PDF into Markdown (enabling full OCR for text and tables when needed).
2. Create a GitHub Gist:
   Paste the Markdown into a public Gist on GitHub. Use the “clone as https” option to copy the link.
3. Add to Cursor:
   Utilize the @Docs > Add New Doc feature in Cursor; assign a clear name, set the entry point and prefix, and index the document.
4. Utilize in Prompts:
   Reference the document using @Doc <AssignedName> to ask the AI specific questions about your material.

Tip: For those using Raycast, a GitHub Gist extension can simplify access and searching.

Incorporating GitHub Repository Content

1. Extract Repository Content:
   Tools like uithub.com allow you to extract specific file types (e.g., Markdown files by appending ?ext=md).
2. Create a Consolidated Gist:
   Merge relevant files (such as README and examples) into a single Markdown document — keeping its size manageable (ideally under 60,000 tokens).
3. Add This Data to Cursor:
   Follow the same process as for PDFs: use the @Docs > Add New Doc feature, configure your document’s parameters, and index it.

Integrating external documentation in this way allows your AI to reference and leverage a broader context, dramatically improving its performance and accuracy.

4. Be Nice to AI

Politeness enhances your interactions with large language models. Studies (such as “Should We Respect LLMs?”) suggest that courteous language — using names and phrases like “please” and “thank you” — improves clarity and compliance. When crafting prompts, a friendly tone may yield better, more effective results.

5. Named Cursor Rules

Proper naming conventions for your cursor rules make them easier to reference and share. For example, Named Cursor Rules on GitHub illustrate how descriptive names streamline collaboration and simplify troubleshooting.

6. Giving Cursor Access to Tooling and Prompt Orchestration

Advanced tooling integration can transform Cursor into a strategic AI collaborator. The devin.cursorrules repository shows how to elevate Cursor (or Windsurf) with agentic capabilities — allowing it to plan, self-evolve, and execute complex tasks. This orchestration empowers your AI to address challenges more holistically rather than simply reacting to commands.

7. Useful Extensions

Enhance your Cursor workflow with dynamic extensions like SpecStory. This extension captures, stores, and streams your AI coding conversations — providing a continuous “consciousness” that helps maintain context across sessions.

8. Key Tips for Using Cursor, Gemini, and Claude

Below is a Markdown summary that outlines additional best practices for managing long codebases by harmonizing your AI collaborators (Cursor, Gemini, and Claude):

This guide provides a practical approach to managing long codebases by leveraging your AI collaborators through the Cursor IDE. Here are some key tips:
---
## 1. Project Setup: Laying the Foundation
### 1.1. Define Rules with `.cursorrules`
- **Purpose:**  
  Sets the guidelines for how you and your AI helpers (Gemini and Claude) will collaborate.  
- **What to Include:**  
  - An "initialization" step that always loads key project documentation (e.g., `project_overview.md` and `task_list.md`).
  - Operational protocols, broken down into:
    - Preparing a **MECE task breakdown** before taking action.
    - Guidelines for code changes such as reviewing relevant code, preserving functionality, and maintaining type safety.
  - Safety requirements (e.g., always preserve proper error handling and document changes).
  - Priority sources and mode settings (like “base” for routine tasks and “enhanced” for complex problems).
#### Example `.cursorrules`
```json
{
    "rules": {
        "context_initialization": {
            "description": "Starting point for each interaction",
            "steps": [
                "ALWAYS read `.notes/project_overview.md` and `.notes/task_list.md`"
            ]
        },
        "operational_protocol": {
            "description": "How to approach tasks",
            "before_action": [
                "Create a MECE task breakdown"
            ],
            "code_changes": [
                "Read relevant code sections before editing",
                "Preserve existing functionality",
                "Maintain type safety"
            ]
        },
        "safety_requirements": [
            "NEVER break type safety",
            "ALWAYS maintain proper error handling",
            "ALWAYS document new code"
        ],
        "priorities": [
            {
                "source": ".notes/",
                "weight": 1.0
            }
        ],
        "modes": {
            "base": {
                "description": "For routine tasks"
            },
            "enhanced": {
                "description": "For complex problems"
            }
        },
        "project_directives": {
            "name": "my_project",
            "ai_first": true
        }
    }
}

1.2. Control File Visibility with .cursorignore

• Purpose:
  Just as with a .gitignore, list the files and directories the AI should skip.
• Example:

/node_modules
/build
/temp
.DS_Store

1.3. Establish Shared Documentation with .notes

• Purpose:
  Use the .notes folder as a central hub for all documentation and shared context.
• Key Files to Include:
• project_overview.md: High-level project goals, architecture, and sample user journeys.
• task_list.md: A detailed breakdown of tasks, statuses, priorities, and notes.
• directory_structure.md: An overview of the project’s file layout.
• meeting_notes.md: Logs of interactions and decisions made with your AI collaborators.

2. Managing Shared Context

2.1. Populate project_overview.md

• Function:
  Provides an “elevator pitch” of your project.
• Contents Should Include:
• Project goal and high-level architecture.
• A summary of key technologies (e.g., front-end frameworks, backend services).
• Sample user journeys.

2.2. Use task_list.md for Planning

• Function:
  Acts as a live to-do list for all project tasks.

How to Organize:

• High Priority: Tasks in progress or needing immediate attention.
• Medium/Low Priority: Tasks planned for later stages.
• Completed: Tasks that have been successfully finished.

2.3. Generate directory_structure.md

• Function:
  Provides a map of your project’s layout to help the AI understand where each component lives.
• Example Script:
  This sample script can automate this directory generation.

3. Prompt Mastery and Focusing Conversations

3.1. Importance of Context

• Focus:
  Context directs the AI to the relevant parts of the codebase.
• Accuracy & Consistency:
  Ensures responses align with your project’s goals and architecture.

3.2. Leverage the @ Symbol

• Usage:
  Use @ to reference or focus on specific documents or code sections.
• Examples:
• @components/Button.tsx: "Let’s review this component."
• @.notes/task_list.md: "Check the current tasks and priorities."
• @.notes/project_overview.md: "Recall the project’s overall goals and architecture."

3.3. Best Practices for Prompting

• Be Specific:
  Pinpoint the exact section or function instead of making general requests.
• Use MECE:
  Break complex tasks into clear, non-overlapping parts.
• Iterate & Refine:
  Engage in dialogue and ask follow-up questions.
• Ask “Why”:
  Request explanations to understand the AI’s reasoning.
• Explore “What If” Scenarios:
  Evaluate hypothetical questions for alternative approaches.

4. Advanced Tips: Elevate Your AI Collaboration

• Maintain a “Consciousness Stream”:
  Log all interactions in meeting_notes.md for continuity.
• Employ Different Modes:
  Switch between “base” and “enhanced” modes depending on task complexity.
• Start Fresh if Needed:
  Begin a new conversation when things get tangled.
• Adopt a Production Mentality:
  Treat your project as a real production system, sharing terminal outputs and logs for iterative improvement.

By following these detailed tips, you can create a well-structured digital workshop where you and your AI collaborators work in harmony, ensuring your long codebases remain manageable and maintain their integrity throughout development.

Conclusion

Maximizing your Cursor use is about uniting systematic planning, advanced prompting, robust tooling configurations, and intelligent documentation integration. By mastering chain-of-thought and few-shot prompting, refining your .cursorrules, incorporating comprehensive external data (repositories and PDFs), and maintaining an effective, polite dialogue with your AI, you build an environment where both human and machine learn and evolve together.

Remember, success comes from collaboration — sharing these best practices multiplies our collective productivity. Happy coding, and may your Cursor always remain optimized and context-rich!