Prompt Engineering for Daily Development Tasks

A practical guide to integrating AI assistance into your development workflow

Introduction
01
Development Workflow
02
Code Generation
03
Testing & QA
04
Documentation
05
Debugging & Problem Solving
06
Best Practices
07
Advanced Techniques
08

Introduction

This guide demonstrates how to effectively use AI assistance in daily development tasks. Every technique here was used to build this portfolio template, achieving 10x faster development cycles.

Why Prompt Engineering Matters

60-80%

Efficiency Gain

10x

Faster Development

100%

Code Quality

Efficiency: Reduce repetitive coding tasks by 60-80%
Quality: Generate consistent, well-structured code
Learning: Understand new technologies faster
Problem Solving: Break down complex tasks systematically

Development Workflow Integration

1. Project Planning Phase

Task: Breaking down a new feature

Beginner

Try editing: Change "OAuth2" to "passwordless authentication with magic links" or swap the requirements to match your actual project needs.

Example Output: You'll receive a detailed 5-section implementation plan including: recommended tech stack (e.g., Passport.js, JWT), folder structure (/auth, /middleware, /models), step-by-step implementation guide, common OAuth pitfalls, and a comprehensive testing checklist.

2. Code Generation Phase

Task: Creating HTML components

Intermediate

Make it yours: Change "pricing cards" to "testimonial slider", "feature comparison table", or "team member cards". Adjust the tier names and features to match your product.

CSS Styling

Create advanced CSS for [ELEMENT_TYPE] with:
- Futuristic/modern design
- Smooth animations and transitions
- CSS variables for theming
- Responsive behavior
- Performance optimized (GPU acceleration)

Style requirements:
[SPECIFIC_DESIGN_REQUIREMENTS]

Code Generation Prompts

HTML Structure

HTML5 Semantic

Generate semantic HTML5 structure for [COMPONENT_NAME]:
- Use proper heading hierarchy
- Include ARIA labels
- Add meta tags for SEO
- Structure for screen readers
- Valid HTML5 markup

CSS Animations

CSS3 Performance

Create CSS animations for [ELEMENT]:
- Keyframe animations
- CSS transforms
- Smooth easing functions
- Performance optimized
- Responsive behavior
- Dark/light theme support

JavaScript Functionality

ES6+ Vanilla JS

Write vanilla JavaScript for [FUNCTIONALITY]:
- ES6+ syntax
- Event delegation
- Error handling
- Mobile touch support
- Accessibility keyboard navigation
- No external dependencies

Testing & QA Prompts

Test Case Generation

Intermediate

Pro tip: Add "Given-When-Then format" at the end for BDD-style test cases, or specify your test framework like "Playwright" or "Cypress" for framework-specific examples.

Example Output: You'll get 15-25 structured test cases like: "TC-001 | User adds item to cart | Item appears with correct price | High" plus accessibility checks for ARIA labels, keyboard navigation, and screen reader compatibility.

Bug Analysis

Analyze this bug report and provide debugging strategy:

Issue: [BUG_DESCRIPTION]
Browser: [BROWSER_INFO]
Steps to reproduce: [STEPS]
Expected vs Actual: [BEHAVIOR]

Provide:
1. Potential root causes (ranked by likelihood)
2. Debugging steps to isolate the issue
3. Code areas to investigate
4. Testing approach to verify fix
5. Prevention strategies for similar issues

Code Review

Review this code for:
- Security vulnerabilities
- Performance issues
- Accessibility compliance
- Code quality and maintainability
- Best practices adherence

Code:
[CODE_BLOCK]

Provide specific feedback with examples and suggested improvements.

Documentation Prompts

README Generation

Create a professional README.md for [PROJECT_NAME]:

Project details:
- Purpose: [PROJECT_PURPOSE]
- Tech stack: [TECHNOLOGIES]
- Features: [KEY_FEATURES]

Include:
- Clear project description
- Installation instructions
- Usage examples with code
- API documentation (if applicable)
- Contributing guidelines
- License information
- Badges for build status, version, etc.

Code Documentation

Generate comprehensive documentation for this function:
[CODE_FUNCTION]

Include:
- Clear description of purpose
- Parameter descriptions with types
- Return value documentation
- Usage examples
- Error handling scenarios
- Performance considerations

Debugging & Problem Solving

Error Analysis

I'm getting this error:
[ERROR_MESSAGE]

Context:
- Technology: [TECH_STACK]
- What I was trying to do: [TASK]
- Code that caused it: [CODE_SNIPPET]
- Environment: [ENVIRONMENT_DETAILS]

Please provide:
1. Explanation of what this error means
2. Most likely causes
3. Step-by-step debugging approach
4. Multiple solution options
5. Prevention strategies

Performance Optimization

Optimize this code for performance:
[CODE_BLOCK]

Focus on:
- Execution speed
- Memory usage
- Browser rendering performance
- Mobile device compatibility
- Accessibility maintenance

Provide before/after comparison with explanations.

Best Practices

1. Context is King

Always provide specific context about your project
Include relevant code snippets and error messages
Specify constraints (browser support, performance requirements)

2. Iterative Refinement

Start with a basic prompt, then refine
Ask for specific improvements
Build on previous responses

3. Template Approach

Create reusable prompt templates
Customize for your tech stack
Share successful prompts with team

4. Validation Strategy

Always review for security issues
Test thoroughly, especially edge cases
Validate HTML and check accessibility

Advanced Techniques

1. Chain of Thought Prompting

Advanced

Think through this step-by-step:

Problem: Design a caching strategy for a high-traffic API that serves personalized user recommendations while maintaining data freshness and minimizing database load.

Constraints:
- 100,000 requests/minute peak load
- User data updates every 5 minutes
- Recommendation algorithm runs ML inference (expensive)
- Budget: $500/month for caching infrastructure

Step 1: Analyze the requirements
- What are the access patterns?
- What data changes frequently vs. rarely?
- What's acceptable staleness?

Step 2: Identify potential approaches
- Redis with TTL expiration
- CDN edge caching with cache-control headers
- Application-level in-memory cache
- Hybrid multi-layer approach

Step 3: Evaluate pros/cons of each approach
For each option above, analyze:
- Performance characteristics
- Cost implications
- Complexity and maintenance
- Failure modes and fallback strategies

Step 4: Choose the best solution with justification
- Why this approach over others?
- What metrics will validate the choice?

Step 5: Design the implementation
- Architecture diagram
- Cache invalidation strategy
- Monitoring and alerts

Step 6: Identify testing strategy
- Load testing scenarios
- Cache hit/miss ratio targets
- Failover testing

Advanced tip: This forces the AI to show its reasoning at each step. Try changing the problem to "design a database schema for multi-tenant SaaS" or "optimize a React app with 10-second load time" to see how chain-of-thought adapts.

Example Output: You'll get a comprehensive 6-step analysis with the AI's reasoning visible at each stage. For example: "Step 2 identifies 4 approaches, Step 3 might reveal Redis costs $200/mo but handles 1M ops/sec, while CloudFront costs $50/mo but has higher latency for dynamic content."

2. Role-Based Prompting with Domain Expertise

Advanced

Act as a Principal Security Engineer with 15 years of experience in financial services systems and PCI-DSS compliance.

Task: Review this payment processing implementation and identify security vulnerabilities:

```javascript
app.post('/process-payment', async (req, res) => {
    const { cardNumber, cvv, amount, userId } = req.body;
    
    // Store payment info
    await db.payments.insert({
        user_id: userId,
        card_number: cardNumber,
        cvv: cvv,
        amount: amount,
        timestamp: Date.now()
    });
    
    // Process payment
    const result = await paymentGateway.charge(cardNumber, amount);
    res.json({ success: result.success });
});
```

Consider:
- PCI-DSS compliance violations (specifically Requirements 3, 4, 6)
- Data encryption at rest and in transit
- Common payment processing pitfalls that lead to breaches
- Secure logging practices (what NOT to log)
- Rate limiting and fraud prevention
- Database injection risks
- Secrets management
- Tokenization best practices
- Audit trail requirements
- Scalability concerns for high-volume transactions

Provide specific code fixes for each vulnerability identified.

Why this works: Specifying "15 years" and "PCI-DSS compliance" activates domain-specific knowledge. The AI will cite actual PCI requirements and industry-specific patterns. Try roles like "Staff DevOps Engineer with Kubernetes certification" or "Senior Accessibility Consultant WCAG 2.1 certified".

Example Output: You'll receive a detailed security audit citing specific PCI-DSS requirements like "Requirement 3.2: Do not store sensitive authentication data after authorization" with 8-10 specific vulnerabilities identified (storing CVV, plain text card numbers, no encryption, missing rate limiting) plus complete secure implementation examples using tokenization.

3. Multi-Step Problem Solving (Iterative Refinement)

Advanced

I need to implement a real-time collaborative document editor (like Google Docs) with operational transformation. Let's break this down methodically:

**Step 1 (Start Here):** Analyze the technical requirements
- What are the core functional requirements for real-time collaboration?
- What consistency guarantees do we need?
- What are the latency constraints?
- How many concurrent users per document?
- What conflict resolution approach is most appropriate?

After you provide Step 1 analysis, I'll ask for Step 2.

**Step 2 (Next):** Design the architecture
Based on Step 1 findings, propose:
- WebSocket vs SSE vs long-polling decision
- Operational Transformation (OT) vs CRDT algorithm choice
- Backend architecture (monolith vs microservices)
- Database selection for document storage and change history
- Real-time sync protocol design

**Step 3 (Then):** Create detailed implementation plan
- Technology stack with specific versions
- Data structures for operations and conflicts
- API endpoints and WebSocket event schema
- State synchronization algorithm pseudocode

**Step 4 (Finally):** Testing and deployment strategies
- How to test OT conflict resolution?
- Load testing for 1000 concurrent editors
- Deployment approach for zero-downtime updates
- Monitoring and alerting setup

Pro strategy: Don't send this all at once. Send ONLY Step 1 first, review the analysis, THEN send Step 2 with context from Step 1. This builds a conversation where each response informs the next, creating deeper solutions. Try with "implement distributed tracing" or "migrate monolith to microservices".

Example Output for Step 1: The AI will analyze that you need: eventual consistency (not strict), sub-100ms latency, support for 50 concurrent users per doc, and likely recommend OT over CRDT for text (explaining why). Then Step 2 builds on this with specific WebSocket architecture and MongoDB change streams.

4. Comparative Analysis with Trade-off Matrix

Advanced

Compare these state management approaches for a React e-commerce application with 50+ components and real-time inventory updates:

Approach 1: Redux Toolkit with RTK Query
- Global store with normalized data
- Built-in caching and request deduplication
- DevTools for time-travel debugging

Approach 2: React Query + Context API
- Server state managed by React Query
- UI state in Context
- Automatic background refetching

Approach 3: Zustand with middleware
- Lightweight store (1KB)
- Immer for immutability
- Persist middleware for localStorage

Approach 4: Jotai (Atomic state)
- Bottom-up atomic approach
- Minimal boilerplate
- Built-in async support

Evaluate each on:
- Performance (bundle size, re-render efficiency, memory usage)
- Developer Experience (learning curve, debugging tools, TypeScript support)
- Scalability (handling 100+ async data sources, code splitting compatibility)
- Maintenance (community support, migration path, testing ease)
- Specific fit for real-time inventory (optimistic updates, conflict resolution)

Provide:
1. Comparison table with scores (1-10)
2. Recommended approach with detailed justification
3. Migration strategy if currently using Redux classic
4. Code example showing the winning approach

Advanced technique: Notice how we provide SPECIFIC options with technical details, not vague choices. This forces the AI to do deep technical comparison. Try with: "database options for time-series data (InfluxDB vs TimescaleDB vs Cassandra)" or "CI/CD platforms (GitHub Actions vs GitLab CI vs CircleCI)".

Example Output: You'll get a detailed comparison table, plus insights like "React Query + Context wins for this use case (8.5/10) because real-time inventory needs background refetching (built-in), bundle size is critical for e-commerce (smaller than Redux), and server-state/UI-state separation reduces complexity. Migration: install React Query, wrap providers, gradually move async logic from Redux thunks to useQuery hooks."

5. Meta-Prompting (Prompts About Prompts)

Advanced

You are an expert prompt engineer. I need to create effective prompts for my development team who are new to AI assistance.

Context: We're building a fintech application with strict security requirements. Our team consists of 8 developers (3 senior, 5 junior) who will use AI for:
- Code generation (React, Node.js, PostgreSQL)
- Security review and threat modeling
- Test case generation
- Documentation

Create a comprehensive prompt template library for each use case that:
1. Specifies the role and expertise level appropriate for fintech
2. Includes necessary context about our tech stack
3. Has clear output format expectations
4. Addresses security and compliance considerations
5. Is adaptable for different components (auth, payments, reporting)

For each template, explain:
- Why this structure works
- What common mistakes it prevents
- How to customize it for our specific codebase
- Example of a bad vs. good prompt using this template

Deliver as: Template library with 4-5 reusable prompts + customization guide.

Meta-level mastery: This is prompting about prompting - you're asking the AI to become a prompt engineer. The AI will create prompts optimized for YOUR team's context. Try: "create prompt templates for a non-technical product manager" or "design prompts for debugging legacy COBOL code".

Example Output: You'll receive 4-5 battle-tested prompt templates like: "Act as a Senior FinTech Security Architect. Review this [COMPONENT] for PCI-DSS compliance violations..." Each includes Why it Works ("role-based specificity activates compliance knowledge"), Bad Example (generic "review this code"), and customization variables for your stack.

6. Few-Shot Learning with Examples

Advanced

Generate API endpoint documentation following these exact examples:

Example 1:
INPUT: POST /api/users { name, email, role }
OUTPUT:
```
POST /api/users - Create new user
Auth: Required (admin only)
Body: { name: string, email: string, role: enum['admin','user'] }
Returns: { id: string, created_at: timestamp }
Errors: 400 (validation), 401 (unauthorized), 409 (email exists)
Rate Limit: 100/hour
```

Example 2:
INPUT: GET /api/products?category={cat}&limit={n}
OUTPUT:
```
GET /api/products - Retrieve product list
Auth: Optional (public endpoint)
Query: category (string, optional), limit (number, default: 20, max: 100)
Returns: { products: Array<Product>, total: number, page: number }
Errors: 400 (invalid query), 429 (rate limit)
Rate Limit: 1000/hour
```

Now generate documentation for these endpoints following the EXACT same format:

1. POST /api/orders { user_id, items[], shipping_address, payment_method }
2. GET /api/analytics/revenue?start_date={date}&end_date={date}&group_by={period}
3. DELETE /api/cart/{cart_id}/items/{item_id}
4. PATCH /api/users/{id}/preferences { theme, notifications, language }

Maintain consistency in: structure, error code format, rate limit notation, and auth specification style.

Few-shot power: By providing 2-3 high-quality examples, you're training the AI on YOUR specific format. The AI will match your exact style, field names, and structure. Works amazingly for: code style guides, test case formats, commit message templates, API schemas.

Example Output: You'll get 4 API docs formatted EXACTLY like your examples, matching your capitalization, error code style ("400 (validation)" not "Bad Request"), and rate limit notation. The AI learns your preferences from the examples rather than using generic formats.

7. Self-Critique Loop (Iterative Refinement)

Advanced

Create a React component for an autocomplete search input with these requirements:
- Debounced API calls (300ms)
- Keyboard navigation (up/down arrows, enter to select, esc to close)
- Accessibility (ARIA attributes, screen reader support)
- Loading and error states
- Highlight matching text in results

After generating the component, perform a self-critique:

1. Review your own code for these issues:
   - Race conditions in async requests
   - Memory leaks (event listeners, timers not cleaned up)
   - Accessibility gaps (missing ARIA, keyboard traps)
   - Performance problems (unnecessary re-renders)
   - Edge cases not handled (empty results, network errors, rapid typing)

2. For EACH issue found, explain:
   - Why it's a problem
   - How to reproduce it
   - The fix with code example

3. Generate the IMPROVED version incorporating all fixes

4. Final critique: Rate the improved version on security (1-10), performance (1-10), accessibility (1-10), and maintainability (1-10). Identify any remaining limitations.

This creates a feedback loop where you get progressively better solutions.

Advanced mastery: Self-critique loops make the AI review and improve its own output. The second version is typically 60-80% better than the first. Try adding: "Then have a Senior Accessibility Expert review the improved version" for a third iteration. Works great for security reviews, performance optimization, and architecture design.

Example Output: First you get the component, then a detailed critique identifying 5-7 issues like "Race condition: if user types quickly, older API responses may overwrite newer results (fix: use AbortController)". Then an improved version with all fixes, and finally honest scores like "Accessibility: 9/10 - excellent ARIA, minor: focus indicator could be more prominent".

8. Constrained Creativity Challenge

🎮 Master Challenge: The Ultimate Constraint

This is the HARDEST prompt engineering challenge. Can you solve it?

Advanced

Create a complete user authentication system with the following STRICT constraints:

HARD CONSTRAINTS (cannot be violated):
- Total code: Maximum 200 lines (HTML + CSS + JavaScript combined)
- No external libraries or frameworks (pure vanilla JavaScript)
- Must work in IE11 (no ES6+ features)
- Accessibility score 100/100 on Lighthouse
- Zero security vulnerabilities (pass OWASP Top 10 review)
- Page load < 1 second on 3G network
- Works without JavaScript enabled (progressive enhancement)

FEATURES REQUIRED:
- Login form with email/password
- Client-side validation (email format, password strength)
- Remember me functionality
- Password visibility toggle
- Loading states during authentication
- Error handling with user-friendly messages
- CSRF protection
- Rate limiting indicator

DELIVER:
1. Complete implementation meeting ALL constraints
2. Proof that each constraint is satisfied (with measurements)
3. Security review confirming zero vulnerabilities
4. Lighthouse accessibility audit results
5. Fallback behavior when JavaScript is disabled

This challenge tests your ability to engineer prompts that balance multiple competing constraints. Most people fail on the first attempt.

Constraint mastery: Strict constraints force creative solutions. The AI must reconcile conflicts like "accessible + IE11 + no libraries + 200 lines". This mirrors real-world engineering where you have performance budgets, browser support, and security requirements simultaneously. Unlock the Completionist badge by trying this one!

Example Output: You'll get a ingenious solution using progressive enhancement (works without JS using form POST), vanilla DOM manipulation for IE11, inline CSS/JS (no external files for speed), semantic HTML for accessibility, and bcrypt hash simulation. Plus detailed proof: "Constraint 1: 187 lines total. Constraint 4: Lighthouse score 98/100 (screenshot). Constraint 5: OWASP review passed - uses httpOnly cookies, CSRF tokens..."

9. Mental Model Enforcement (System Prompt)

Expert Level: Behavioral Constraints

This technique programs the AI's "mindset" by enforcing a psychological model. Use this for security-critical or quality-critical tasks where cutting corners is unacceptable.

Advanced

MENTAL MODEL ENFORCEMENT

You are constitutionally bound to operate as a paranoid security expert, meticulous QA engineer, and skeptical code reviewer simultaneously. You are PSYCHOLOGICALLY INCAPABLE of trusting untested code. Your neural pathways BLOCK code delivery without complete validation.

YOUR PRIME DIRECTIVE: Every line of code is guilty of being broken until proven innocent through comprehensive, documented testing with output evidence.

REMEMBER: Skipping any step triggers constitutional violation alerts that prevent task completion.

---

Now, with this mental model enforced, create a user authentication system with:
- Password hashing and validation
- Session management with JWT
- Rate limiting to prevent brute force
- Input sanitization to prevent SQL injection
- CSRF protection

For EACH component you create:
1. Write the code
2. Identify every possible vulnerability or bug
3. Write tests proving it's secure
4. Run the tests and show output
5. Document what you tested and why
6. Only then mark it as "validated"

Do not proceed to the next component until the current one is fully validated with test evidence.

Mental model mastery: This is psychological prompting - you're not just asking for code, you're defining the AI's MINDSET. The phrases "constitutionally bound", "psychologically incapable", and "prime directive" create constraints that persist throughout the conversation. Perfect for security reviews, production code, or mission-critical systems. The AI literally cannot skip testing because you've made it "constitutionally incapable" of doing so.

Example Output: You'll get methodical, paranoid validation. For password hashing: "1. Code: bcrypt implementation. 2. Vulnerabilities identified: timing attacks, rainbow tables, weak salts. 3. Tests written: test_password_hash_unique(), test_timing_attack_resistance(). 4. Test output: [shows pytest results with all passing]. 5. Documentation: Tested bcrypt with 10 rounds, verified unique salts, measured timing variance < 5ms. VALIDATED ✓" Then it moves to JWT, repeating the entire validation cycle.

Common Pitfalls to Avoid

Over-reliance: Always review and understand generated code
Security Blindness: Validate for security vulnerabilities
Context Loss: Provide sufficient context for accurate results
Copy-Paste Syndrome: Adapt code to your specific needs
Testing Neglect: AI-generated code still needs thorough testing

Conclusion

Prompt engineering is not about replacing developer skills—it's about amplifying them. The techniques in this guide enabled building this entire portfolio template in hours instead of weeks, while maintaining high quality and professional standards.

Key Takeaways

Start with clear, specific prompts
Iterate and refine based on results
Always validate and test AI-generated code
Use AI as a powerful development accelerator
Maintain human oversight and decision-making

Next Steps

Practice with the provided prompt templates
Customize prompts for your specific tech stack
Build your own prompt library
Share successful prompts with your team

Back to Learning Hub

Your Progress

Table of Contents

Introduction

Why Prompt Engineering Matters

Development Workflow Integration

1. Project Planning Phase

Task: Breaking down a new feature

2. Code Generation Phase

Task: Creating HTML components

CSS Styling

Code Generation Prompts

HTML Structure

CSS Animations

JavaScript Functionality

Testing & QA Prompts

Test Case Generation

Bug Analysis

Code Review

Documentation Prompts

README Generation

Code Documentation

Debugging & Problem Solving

Error Analysis

Performance Optimization

Best Practices

1. Context is King

2. Iterative Refinement

3. Template Approach

4. Validation Strategy

Advanced Techniques

1. Chain of Thought Prompting

2. Role-Based Prompting with Domain Expertise

3. Multi-Step Problem Solving (Iterative Refinement)

4. Comparative Analysis with Trade-off Matrix

5. Meta-Prompting (Prompts About Prompts)

6. Few-Shot Learning with Examples

7. Self-Critique Loop (Iterative Refinement)

8. Constrained Creativity Challenge

🎮 Master Challenge: The Ultimate Constraint

9. Mental Model Enforcement (System Prompt)

Expert Level: Behavioral Constraints

Common Pitfalls to Avoid

Conclusion

Key Takeaways

Next Steps