Free yourself from grunt work — and focus on the creative parts of software development.
Introduction: Why Developers Waste Time on “Glue Code”
Every developer knows the feeling: spending hours writing boilerplate, setting up tests, or hunting for that perfect regex. None of these tasks make you a better engineer — they just eat away your focus.
Here’s the good news: AI coding assistants like GitHub Copilot, ChatGPT, Tabnine, and Codeium are finally good enough to take this work off your plate. The trick isn’t to let AI do everything — it’s knowing which everyday tasks to delegate so you can focus on architecture, problem-solving, and creativity.
Let’s break down the 8 coding tasks that AI should be handling for you starting today.
1. Writing Boilerplate Code
The Pain:
From setting up React components to initializing Express routes, boilerplate is repetitive.
How AI Helps:
- Copilot can autocomplete entire scaffolds.
- ChatGPT can generate templates (e.g., CRUD endpoints, Redux slices).
Example Prompt:
“Generate a REST API endpoint in Express.js with GET, POST, PUT, DELETE handlers for a User resource, following clean architecture.”
Result: Hours saved on setup — you focus on the unique logic.
2. Regex and String Manipulation
The Pain:
Regex is powerful but unreadable. Writing it manually = trial and error.
How AI Helps:
- Translate English → Regex.
- Provide readable explanations of cryptic patterns.
Example Prompt:
“Write a regex that validates email addresses and explain it line by line.”
Result: Working regex in seconds, with documentation.
3. Unit Test Generation
The Pain:
Developers forget edge cases. Writing tests manually is slow.
How AI Helps:
- AI enumerates normal, edge, and invalid cases.
- Generates framework-specific tests (Jest, PyTest, JUnit).
Example Prompt:
“Write Jest unit tests for this function that validates passwords, including edge cases like empty string and SQL injection attempts.”
Result: Stronger coverage with less effort.
4. Documentation and Comments
The Pain:
Nobody likes writing docs. Most codebases suffer from missing or outdated comments.
How AI Helps:
- Auto-generates docstrings and inline comments.
- Can even create Markdown documentation for APIs.
Example Prompt:
“Add docstrings to this Python file following Google style guide.”
Result: Self-documenting code without the pain.
5. Data Transformation and Parsing
The Pain:
Transforming JSON → CSV, cleaning up logs, mapping nested objects… these tasks are tedious.
How AI Helps:
- Generate one-off scripts.
- Explain unfamiliar data formats.
Example Prompt:
“Convert this nested JSON into a flat CSV using Python. Include error handling.”
Result: Quick scripts that get the job done without brain-drain.
6. Code Reviews and Refactoring
The Pain:
Manual code reviews catch style issues and logic gaps — but take hours.
How AI Helps:
- Highlight bad naming, redundant code, and complexity.
- Suggest refactoring patterns (e.g., DRY, SRP).
Example Prompt:
“Review this JavaScript function for readability and refactor it into smaller functions.”
Result: Cleaner, more maintainable code.
7. Error Explanation and Debugging
The Pain:
Stack traces can feel like hieroglyphics. Debugging eats hours.
How AI Helps:
- AI translates errors into plain English.
- Suggests probable causes and fixes.
Example Prompt:
“This error occurs in my Node.js app: UnhandledPromiseRejectionWarning: TypeError. Explain the cause and suggest fixes.”
Result: Faster debugging — less Googling.
8. API Integration Snippets
The Pain:
Every API has slightly different auth, pagination, and error rules. Copy-pasting from docs is time-consuming.
How AI Helps:
- Generates ready-to-use API call code.
- Adds headers, retries, and pagination logic automatically.
Example Prompt:
“Write Python code that calls the GitHub API to fetch all repos for a user, handling pagination and rate limits.”
Result: Production-ready snippets you can extend.
The Bigger Picture: Delegation, Not Automation
Notice what’s missing? AI shouldn’t design your system, decide business logic, or replace your problem-solving skills.
Instead, let AI handle everyday grunt work while you focus on the hard parts of engineering: architecture, tradeoffs, and creativity.
Conclusion: Work Smarter, Not Harder
Developers who embrace AI as a coding partner will write cleaner, faster, and more maintainable code. Those who don’t? They’ll stay stuck in the glue work.
The choice is simple: delegate these 8 everyday coding tasks to AI — and spend your brainpower on the work that really matters.
Call to Action
Which of these tasks do you already let AI handle? And which one will you try next? Share your experience in the comments.
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