Chapter 9
Cursor Playbooks
The most powerful developers aren't those who know every API by heart—they're the ones who know exactly which questions to ask and when. This chapter transforms Cursor from a helpful assistant into a strategic partner by providing battle-tested playbooks: structured, repeatable workflows that guide you through common development challenges with precision and speed.
Think of playbooks as recipes. A recipe doesn't just list ingredients—it tells you when to add them, what order matters, and how to recognize when something's done. Similarly, these playbooks don't just give you prompts; they teach you the rhythm of effective AI-assisted development: when to plan, when to execute, when to test, and when to verify.
Prompt Library: Ready-to-Use Prompts for All Levels
The right prompt makes all the difference between generic AI output and code that feels hand-crafted. These prompts follow a Role-Goal-Context-Constraints (RGCC) structure to ensure Cursor understands not just what you want, but why and how.
🟢 Beginner Level Prompts
At this level, the goal is learning and building confidence. Use these prompts to understand code, generate simple functions, and debug methodically.
Explain Code Like I'm Learning
You are a patient teacher for beginner developers.
Explain this code to me step by step:
[paste code]
Include:
- What each line does and why
- What concepts I should understand first
- How I could modify it safely
- One alternative approach and when to use it
Keep explanations simple and use analogies where helpful.When to use: Encountering unfamiliar patterns, reading inherited code, or onboarding to a new framework.
Generate a Beginner-Friendly Function
Create a [language] function that [specific task]:
Requirements:
- Use descriptive variable names (no single letters)
- Add inline comments explaining the logic
- Include basic error handling for common cases
- Provide 2-3 usage examples with expected outputs
- Write in a style suitable for someone learning [language]
Explain why you chose this approach.Debug Assistant for Beginners
Help me debug this issue systematically:
Error Message: [paste error]
Code Context: [paste relevant code]
Please:
1. Explain what's causing the error in simple terms
2. Show me the exact fix with before/after comparison
3. Teach me how to prevent this type of error in the future
4. Add logging statements to help me understand the flow
Walk me through your reasoning step by step.🟡 Mid-Level Developer Prompts
Mid-level developers need prompts that balance speed with quality. These focus on feature implementation, refactoring, and performance optimization.
Feature Implementation with Context
Implement [feature description] for this [framework] project:
Context: @[related-component] @[similar-feature]
Requirements:
- Follow existing patterns from @[style-guide]
- Add comprehensive error handling with meaningful messages
- Include unit tests covering happy path + 3 edge cases
- Update related documentation in @[docs-file]
- Maintain backward compatibility with version [X]
Provide a brief implementation plan first, then execute.Why it works: The @ symbols ensure Cursor understands your project's conventions. The "plan first" step prevents hallucinated code.
Clean Code Refactoring
You are a senior engineer focused on maintainability.
Refactor @[filename] to improve code quality:
Focus areas:
- Apply SOLID principles (especially Single Responsibility)
- Reduce cyclomatic complexity and nested conditionals
- Extract reusable utilities to @[utils-folder]
- Add TypeScript types where missing
- Improve naming for clarity
Show before/after comparison and explain each major change.
Preserve all existing functionality—add tests to prove it.🔴 Senior/Principal Level Prompts
Senior engineers use Cursor for architecture, decision-making, and complex system design. These prompts emphasize tradeoffs, scalability, and long-term thinking.
System Architecture Design
Design architecture for [system description]:
Context:
@[existing-services]
@[requirements-doc]
@[constraints]
Deliverables:
1. Service boundaries and responsibilities (diagram preferred)
2. API contracts and data flow between services
3. Scalability patterns (caching, load balancing, async processing)
4. Resilience strategies (circuit breakers, retries, fallbacks)
5. Migration strategy from current system (phased approach)
6. Observability plan (metrics, logging, tracing)
7. Technology stack recommendations with justification
Include tradeoffs for key decisions.Architecture Decision Record (ADR) Generation
Generate an Architecture Decision Record for [decision]:
Context: @[system-overview] @[constraints-doc]
Structure:
- **Status**: Proposed/Accepted/Deprecated
- **Problem Statement**: What forces are at play?
- **Decision**: What did we choose and why?
- **Considered Alternatives**:
- Option 1: [pros/cons]
- Option 2: [pros/cons]
- **Consequences**: What becomes easier/harder?
- **Implementation Plan**: Steps with success metrics
- **Risks and Mitigations**: Top 3 risks with fallback plans
Keep technical but accessible to non-engineers.Why ADRs matter: They prevent "why did we do it this way?" questions six months later.
Playbook A: Feature Development (Fast, Safe Delivery)
Goal: Ship a well-tested, documented feature with minimal back-and-forth
Time estimate: 4-8 hours depending on complexity
Phase 1: Planning and Setup (30 minutes)
The biggest mistake in AI-assisted development is jumping straight to code generation. Without clear requirements, AI generates plausible-looking code that solves the wrong problem.
Let's plan the implementation of [feature description]:
Step 1: Requirements Analysis
- Break down the feature into 3-5 user stories
- Identify affected components and services
- List dependencies and prerequisites
- Estimate complexity (S/M/L) and provide reasoning
Step 2: Technical Design
- Design API contracts and data models
- Plan component hierarchy and state management
- Identify reusable patterns from @[similar-features]
- Create implementation checklist
Step 3: Scaffolding
- Generate boilerplate following @[project-standards]
- Set up testing structure (unit + integration)
- Create migration scripts if database changes needed
- Update project documentation skeleton
Provide the plan first. Wait for my approval before generating code.Phase 2: Implementation Loop (2-4 hours)
Never let AI generate 500 lines in one shot. Break implementation into small, verifiable chunks. The rhythm: implement → test → review → commit. Repeat until done.
Implement the next component in our feature plan:
Current Progress: [describe what's done]
Next Task: [specific component/function to build]
Execution:
- Follow the approved design from planning phase
- Reference similar patterns in @[existing-code]
- Include error handling with specific error messages
- Add unit tests for business logic (aim for 80%+ coverage)
- Update integration points with @[related-services]
After implementation:
- Summarize what you built
- List any deviations from the plan and why
- Suggest the next step
Let's work iteratively—I'll review before moving forward.Phase 3: Testing and Validation (1-2 hours)
Generate comprehensive tests for [feature]:
Test Coverage Required:
1. **Unit Tests**
- All public methods
- Happy path for each function
- Edge cases (null, empty, boundary values)
- Error conditions
2. **Integration Tests**
- Feature workflow end-to-end
- Integration with @[dependent-services]
- Database transactions (if applicable)
3. **Edge Cases**
- Concurrent access scenarios
- Large data volumes
- Network failures
- Invalid input combinations
Use existing test patterns from @[test-examples]
Aim for 85%+ coverage of new codePhase 4: Final Review and Documentation (30 minutes)
Prepare [feature] for PR submission:
Pre-PR Checklist:
- Run all tests and fix failures
- Add/update README with usage examples
- Generate API documentation (JSDoc/docstrings)
- Check for unused imports and console logs
- Verify error messages are user-friendly
- Add changelog entry
PR Description Template:
- **What**: One-sentence summary
- **Why**: Business context
- **How**: Technical approach
- **Testing**: How to verify
- **Risks**: Potential issues and mitigations
Generate the PR description and checklist.Playbook B: Bug Fixing (Reproduce → Isolate → Patch)
Goal: Systematically debug issues without guesswork
Time estimate: 1-4 hours depending on complexity
Step 1: Investigation and Root Cause Analysis (30-45 minutes)
Jumping to solutions wastes time. Fifteen minutes of diagnosis prevents band-aid fixes that mask symptoms without addressing causes.
Help me systematically debug this issue:
Bug Report:
- Expected Behavior: [what should happen]
- Actual Behavior: [what's happening]
- Reproduction Steps: [how to trigger]
- Error Message: [paste if available]
Code Context: @[relevant-files]
Investigation Process:
1. Reproduce the issue consistently (provide repro code if needed)
2. Add detailed logging to trace execution flow
3. Analyze root cause with evidence (not assumptions)
4. Explain why this bug wasn't caught earlier
5. Propose the minimal fix (no feature creep)
Provide a diagnosis first. Don't jump to solutions yet.Step 2: Fix Implementation and Testing (1-2 hours)
Based on the diagnosis:
Root Cause: [confirmed issue]
Fix Implementation:
- Show the minimal code change required
- Explain why this fix is safe and complete
- Add regression tests to prevent recurrence
- Improve error handling if the bug was silent
- Add monitoring/alerting if this could happen again
Provide before/after comparison and test cases.Step 3: Postmortem and Prevention (15-30 minutes)
Generate a brief postmortem for this bug:
Template:
- **What Happened**: One-sentence summary
- **Root Cause**: Technical explanation
- **Impact**: Who was affected and how
- **Resolution**: What we changed
- **Prevention**: How we'll catch this earlier next time
(e.g., add validation, improve tests, add monitoring)
- **Follow-ups**: 2-3 action items
Keep it to 200-300 words.One-Page Cheatsheets for Quick Reference
Cheatsheet 1: Cursor AI Essentials
╔═══════════════════════════════════════════════════════════╗
║ CURSOR AI CHEATSHEET – ESSENTIALS ║
╚═══════════════════════════════════════════════════════════╝
🎯 CORE AI FEATURES
┌─────────────────────┬────────────────────────────────────┐
│ Inline Edit │ Ctrl/Cmd + K │
│ Chat/Ask │ Ctrl/Cmd + L │
│ Composer (Agent) │ Ctrl/Cmd + I │
│ Full Composer │ Ctrl/Cmd + Shift + I │
└─────────────────────┴────────────────────────────────────┘
✨ CODE COMPLETION
┌─────────────────────┬────────────────────────────────────┐
│ Accept Suggestion │ Tab │
│ Reject Suggestion │ Esc │
│ Accept Next Word │ Ctrl/Cmd + → │
└─────────────────────┴────────────────────────────────────┘
🔍 CONTEXT SYMBOLS
┌──────────────┬───────────────────────────────────────────┐
│ @filename │ Reference specific file │
│ @function │ Reference specific function │
│ @codebase │ Search entire project │
│ @web │ Pull external documentation │
└──────────────┴───────────────────────────────────────────┘
💡 PRO TIPS
• Use .cursorrules for project-specific AI behavior
• Break large tasks into small steps (iterate)
• Always review AI changes before accepting
• Commit frequently (easy rollback)Cheatsheet 2: Prompt Formula (RGCC Framework)
╔═══════════════════════════════════════════════════════════╗
║ EFFECTIVE PROMPT FORMULA (RGCC) ║
╚═══════════════════════════════════════════════════════════╝
📋 STRUCTURE
1. ROLE (Who should AI be?)
"You are a [senior backend engineer / security expert]"
2. GOAL (What do you want?)
"Design a caching strategy for [specific system]"
3. CONTEXT (What's relevant?)
- Files: @filename
- Requirements: [specific needs]
- Constraints: [limitations, standards]
4. CONSTRAINTS (What are the rules?)
- Must follow patterns in @existing-code
- Use [specific technology]
- Test coverage >80%
✅ GOOD PROMPT EXAMPLE
"You are a senior security engineer.
Audit @api/auth.js for security vulnerabilities.
Context:
- Handles JWT authentication
- Used by 1M+ users
- Must comply with OWASP Top 10
Check for:
- SQL injection vectors
- XSS vulnerabilities
- Authentication bypass
- Rate limiting gaps
For each issue:
- Severity (Critical/High/Medium/Low)
- Exploit scenario
- Code fix"
❌ BAD PROMPT EXAMPLE
"Fix the auth code"Key Takeaways
✅Playbooks Transform AI from Tool to Methodology
Individual prompts are useful; structured workflows are transformational. Playbooks teach process, not just solutions.
ℹ️The RGCC Formula is Universal
Whether you're a junior fixing bugs or a principal architecting systems, specific structured prompts (Role-Goal-Context-Constraints) produce better results.
✅Test-First Workflows Prevent AI Hallucination
Writing tests before implementation gives AI a specification to target and provides immediate validation.
⚠️Break Large Tasks Into Small Steps
Never let AI generate 500 lines at once. Implement component by component, validating after each step.
ℹ️Documentation Becomes Easy
AI excels at generating docs—use it, but always review for accuracy. What took 2 hours now takes 15 minutes.
✅Customization Makes Playbooks Useful
These templates are starting points. Adapt them to your tech stack, conventions, and team context for maximum value.
The next chapter explores how specialized roles—QA teams, product teams, and entire SDLCs—transform when these playbooks become organizational infrastructure rather than just individual tools.