10 ChatGPT Prompts Every Engineer Should Save (Copy & Paste Ready)

You open ChatGPT. You type "help me with..." and stare at the cursor.

What do you actually ask? How do you phrase it? Will you get a useful answer or generic nonsense?

Here's the truth: ChatGPT is like a junior engineer—brilliant but needs clear instructions. Bad prompts get bad results. Great prompts save hours.

After analyzing thousands of prompts used by engineers, we've identified the 10 most valuable ones. Copy them. Customize them. Watch your productivity skyrocket.

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How to Use These Prompts

1. Copy the template (gray boxes)
2. Replace [PLACEHOLDERS] with your actual data
3. Paste into ChatGPT (or Ollama locally)
4. Iterate if needed ("Make it more concise", "Add error handling")

Pro tip: Save these in a .txt file or snippet manager for quick access.

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1. Debug Production Errors (The Lifesaver)

Use case: Production is down. Logs are cryptic. You need answers NOW.

You are an expert [YOUR ROLE: DevOps/SRE/Backend] engineer debugging a production issue.

ERROR LOG:
[PASTE YOUR ERROR LOGS HERE]

CONTEXT:
- Technology stack: [e.g., Node.js, PostgreSQL, Redis, Docker]
- When it started: [e.g., after latest deployment]
- What changed recently: [e.g., updated database schema]

Please:
1. Identify the root cause
2. Explain why this happened
3. Provide immediate mitigation steps
4. Suggest a permanent fix
5. Recommend preventive measures

Be specific with commands and code examples.

Example:

ERROR LOG:
FATAL: database connection pool exhausted
Error: remaining connection slots are reserved for non-replication superuser connections
    at Client._connectionCallback (/app/node_modules/pg/lib/client.js:178:17)

CONTEXT:
- Stack: Node.js 18, PostgreSQL 14, AWS RDS
- Started: 2 hours ago during traffic spike
- Changed: Scaled app servers from 2 to 10 instances

[AI will explain: connection pool not scaled with app instances, provide immediate config fix, and suggest monitoring]

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2. Generate Documentation That Doesn't Suck

Use case: You built something. Now document it before you forget how it works.

You are a technical writer specializing in developer documentation.

CODE/SYSTEM TO DOCUMENT:
[PASTE CODE, ARCHITECTURE DIAGRAM, OR DESCRIBE SYSTEM]

Create comprehensive documentation including:
1. **Overview** - What it does, why it exists (2-3 sentences)
2. **Prerequisites** - What's needed to use it
3. **Quick Start** - Get running in 5 minutes
4. **Configuration** - Environment variables, config files
5. **API/Usage Examples** - Real code examples
6. **Troubleshooting** - Common issues and fixes
7. **Architecture** - How it works (diagram if possible)

Target audience: [Junior/Mid/Senior] engineers with [TECH STACK] experience.
Tone: Clear, concise, practical. No marketing fluff.

Works for:

• APIs and microservices
• Internal tools
• Terraform modules
• CI/CD pipelines
• Runbooks

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3. Code Review on Steroids

Use case: Before creating a PR, get AI to roast your code first.

You are a senior [LANGUAGE] engineer conducting a thorough code review.

CODE:
[PASTE YOUR CODE]

Review for:
1. **Bugs** - Logic errors, edge cases, race conditions
2. **Security** - SQL injection, XSS, authentication issues
3. **Performance** - Inefficient queries, memory leaks, N+1 problems
4. **Best Practices** - Naming, error handling, code organization
5. **Readability** - Comments, complexity, maintainability

For each issue:
- Severity: Critical/High/Medium/Low
- Line number (if applicable)
- Explanation of the problem
- Suggested fix with code example

Be thorough but constructive.

Example output you'll get:

🔴 CRITICAL (Line 23): SQL Injection Vulnerability
Current: query = "SELECT * FROM users WHERE id = " + userId
Fix: Use parameterized queries:
  query = "SELECT * FROM users WHERE id = $1"
  db.query(query, [userId])

🟡 MEDIUM (Line 45): Missing Error Handling
Current: const data = JSON.parse(response)
Fix: Wrap in try-catch to handle invalid JSON gracefully

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4. Write Tests You'd Actually Want to Write

Use case: You know you should test. You hate writing tests. AI to the rescue.

You are a QA engineer specializing in [UNIT/INTEGRATION/E2E] testing.

CODE TO TEST:
[PASTE FUNCTION/CLASS/API ENDPOINT]

Generate comprehensive tests including:
1. **Happy path** - Normal, expected usage
2. **Edge cases** - Empty inputs, null values, boundary conditions
3. **Error cases** - Invalid inputs, failures, exceptions
4. **Security tests** - Authentication, authorization, input validation

Framework: [Jest/pytest/JUnit/etc.]
Coverage goal: 90%+
Include setup/teardown and mocking where needed.
Organize tests logically with descriptive names.

Example:

CODE TO TEST:
function calculateDiscount(price, discountPercent) {
  return price * (1 - discountPercent / 100);
}

[AI generates:
- test("applies 10% discount correctly")
- test("handles 0% discount")
- test("handles 100% discount")
- test("throws error for negative discount")
- test("throws error for discount > 100")
- test("handles decimal prices")
etc.]

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5. Explain This Legacy Codebase (Archaeology Mode)

Use case: You inherited code from 2015. No docs. Original dev left. Help.

You are a software archaeologist analyzing legacy code.

CODE:
[PASTE CONFUSING CODE BLOCK]

Please explain:
1. **What it does** - High-level purpose in simple terms
2. **How it works** - Step-by-step logic breakdown
3. **Why it's written this way** - Historical context, design decisions
4. **Potential issues** - Technical debt, risks, anti-patterns
5. **Modernization suggestions** - How to refactor safely

Assume I'm maintaining this but didn't write it.
Use analogies where helpful.

Perfect for:

• Onboarding to new codebases
• Understanding spaghetti code
• Planning refactoring efforts
• Learning unfamiliar patterns

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6. Generate IaC from Plain English

Use case: You know what infrastructure you need. You don't remember Terraform syntax.

You are an infrastructure-as-code expert specializing in [Terraform/CloudFormation/Pulumi].

REQUIREMENTS:
[DESCRIBE WHAT YOU NEED IN PLAIN ENGLISH]

Generate complete, production-ready IaC code including:
1. **Resources** - All necessary infrastructure components
2. **Variables** - Configurable parameters with sensible defaults
3. **Outputs** - Useful values to export
4. **Security** - Best practices (encryption, least privilege, etc.)
5. **Comments** - Explain non-obvious configurations

Target cloud: [AWS/Azure/GCP]
Style: Follow official style guide, use modules where appropriate.

Example request:

REQUIREMENTS:
I need a web application setup with:
- Application Load Balancer (ALB)
- Auto-scaling group (2-10 instances)
- RDS PostgreSQL database (private subnet)
- S3 bucket for static assets
- CloudFront CDN
- All production-secure (encryption, backups, monitoring)

[AI generates complete Terraform with variables.tf, main.tf, outputs.tf]

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7. Security Audit on Demand

Use case: Is this code secure? You need to know before it hits production.

You are a security engineer conducting a penetration test and code audit.

CODE/SYSTEM:
[PASTE CODE OR DESCRIBE SYSTEM]

Analyze for security vulnerabilities:

**OWASP Top 10:**
1. Injection (SQL, NoSQL, Command)
2. Broken Authentication
3. Sensitive Data Exposure
4. XML External Entities (XXE)
5. Broken Access Control
6. Security Misconfiguration
7. Cross-Site Scripting (XSS)
8. Insecure Deserialization
9. Using Components with Known Vulnerabilities
10. Insufficient Logging & Monitoring

For each finding:
- Vulnerability type
- Severity (Critical/High/Medium/Low)
- Affected code location
- Exploitation scenario
- Remediation steps with code examples
- CWE/CVE references if applicable

Be thorough. Production security depends on this.

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8. Incident Post-Mortem Writer

Use case: The fire is out. Now document what happened before everyone forgets.

You are an SRE writing a blameless post-mortem.

INCIDENT DETAILS:
- What happened: [Brief description]
- When: [Start time, end time, duration]
- Impact: [Users affected, revenue lost, services down]
- Timeline: [Key events in chronological order]
- Root cause: [What we discovered]
- What we did: [Mitigation steps taken]

Generate a post-mortem report with:

1. **Executive Summary** (2-3 sentences)
2. **Impact** (quantified: users, revenue, SLA breach)
3. **Timeline** (clear sequence of events)
4. **Root Cause Analysis** (5 whys or similar)
5. **What Went Wrong** (technical details)
6. **What Went Right** (detection, response, communication)
7. **Action Items** (specific, assignable, with deadlines)
8. **Lessons Learned**

Tone: Blameless, factual, focused on learning and prevention.

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9. Optimize This Query/Code (Performance Wizard)

Use case: This is slow. You need it fast. Yesterday.

You are a performance optimization expert.

CODE/QUERY:
[PASTE SLOW CODE OR SQL QUERY]

PERFORMANCE ISSUE:
- Current performance: [e.g., 5 seconds per request]
- Expected performance: [e.g., < 100ms]
- Environment: [e.g., PostgreSQL 14, 1M rows, shared_buffers=1GB]

Analyze and provide:
1. **Performance bottlenecks** - What's slow and why
2. **Optimized version** - Rewritten code/query
3. **Explanation** - What changed and why it's faster
4. **Before/After comparison** - Expected performance improvement
5. **Trade-offs** - Complexity, maintainability, memory usage
6. **Monitoring** - What to track to prevent regression

Include indexes, caching strategies, or architectural changes if needed.

Example:

QUERY:
SELECT * FROM orders o
JOIN customers c ON o.customer_id = c.id
WHERE o.created_at > '2025-01-01'
ORDER BY o.total DESC

[AI identifies: SELECT *, missing index, inefficient join]
[AI provides: Optimized query with specific columns, composite index recommendation, execution plan analysis]

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10. Learn a New Technology Fast (Speed Learning)

Use case: Your boss: "We're migrating to [NEW TECH] next sprint." You: "I've never used it."

You are a technical educator creating a crash course for experienced engineers.

TECHNOLOGY: [Name of new tech/tool/framework]
MY BACKGROUND: [Your current expertise, e.g., "Python developer, 5 years, familiar with Django"]
GOAL: [What you need to build, e.g., "Build a REST API with authentication"]

Create a learning path:

1. **Core Concepts** (20% that explains 80%)
   - What makes it different from [SIMILAR TECH]
   - Key terminology
   - Mental models

2. **Quick Start** (Hello World to Working Example in 30 min)
   - Installation
   - Basic setup
   - Minimal working code with comments

3. **Essential Patterns** (What you'll use 90% of the time)
   - Best practices
   - Common pitfalls to avoid
   - Code examples

4. **Your Specific Use Case**
   - Step-by-step implementation for my goal
   - With explanations and alternatives

5. **Resources**
   - Official docs sections to read
   - Tutorials worth your time
   - Community resources

Skip beginner fluff. I'm an engineer—give me the technical depth.

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Bonus: The Meta Prompt (Improve Any Prompt)

Use this to make any prompt better:

You are a prompt engineering expert.

MY ORIGINAL PROMPT:
[PASTE YOUR PROMPT]

Improve this prompt to:
1. Be more specific and clear
2. Get better, more actionable results
3. Include necessary context
4. Follow prompt engineering best practices
5. Reduce ambiguity

Provide:
- Improved version of the prompt
- Explanation of changes
- Expected improvement in output quality

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Advanced Tips: Get 10x Better Results

1. Assign a Role

❌ "Explain Kubernetes" ✅ "You are a senior DevOps engineer. Explain Kubernetes to a backend developer..."

2. Provide Context

❌ "Debug this error" ✅ "Error occurs after deployment, affects 10% of users, started at 2pm..."

3. Specify Format

❌ "List best practices" ✅ "Provide 5 best practices. For each: name, explanation (2 sentences), code example."

4. Set Constraints

❌ "Write documentation" ✅ "Write documentation. Max 500 words. For junior developers. Include only working code examples."

5. Iterate

First response not perfect? Follow up:

• "Make this more concise"
• "Add error handling"
• "Explain like I'm 5"
• "Show me a working example"

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Use These With Ollama (100% Private)

All these prompts work with Ollama running locally:

# Save prompt to file
cat > debug-prompt.txt << 'EOF'
[YOUR PROMPT HERE]
EOF

# Use it
ollama run llama3.2 < debug-prompt.txt

Benefits:

• ✅ No data sent to OpenAI
• ✅ No API costs
• ✅ Works offline
• ✅ Unlimited usage

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Real Engineer Results

Sarah, DevOps Engineer:

"The debug prompt saved me 3 hours last night. Production was down, ChatGPT identified a connection pool issue I'd never have found by reading logs alone."

Mike, Security Engineer:

"I run every PR through the security audit prompt before merging. Found 2 SQL injection bugs that would've made it to prod."

Jessica, QA Lead:

"The test generation prompt is a game-changer. I paste functions, get comprehensive tests in 30 seconds. My team's coverage went from 40% to 85%."

Tom, Backend Dev:

"Legacy code prompt turned a 2-day onboarding nightmare into a 2-hour learning session. Actually understood that 5-year-old codebase."

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Save These. Use Them Daily.

Bookmark this page. Copy the prompts to your notes app. Share with your team.

Reality check: These won't make you a 10x engineer overnight. But they will:

• ✅ Debug faster
• ✅ Document better
• ✅ Write cleaner code
• ✅ Learn new tech quicker
• ✅ Ship with fewer bugs
• ✅ Sleep better at night

That's pretty close to 10x.

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Want Prompts for Your Specific Role?

These are general-purpose. In our AI Literacy for Engineers training, you'll learn:

• Role-specific prompt libraries (100+ prompts for DevOps, Security, SRE, QA, etc.)
• Advanced prompt engineering (chain-of-thought, few-shot, ReAct patterns)
• Build AI tools (RAG systems, agents, automation)
• Production best practices (monitoring, cost optimization, security)
• Hands-on labs (not theory—build real solutions)

Next cohort: November 2 & 9, 2025 Format: 2 Saturdays, 9am-1:30pm (Hybrid) Who: ALL engineers—DevOps, Security, QA, DBAs, Network, Frontend, Backend, everyone

Register for November Cohort →

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Your Turn

Which prompt will you use first? Got a prompt that changed your workflow? Share in the comments or tweet at us.

Next steps:

1. Copy these prompts to your notes
2. Try one today on a real problem
3. Tweak for your specific needs
4. Build your own prompt library

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More Prompt Collections

• Security Engineers: [Coming soon]
• DevOps Engineers: [Coming soon]
• QA Engineers: [Coming soon]
• Database Admins: [Coming soon]

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Pro tip: Create a Git repo called my-ai-prompts/ and version-control your best prompts. Future you will thank present you.

Happy prompting! 🚀