Automating Repetitive Coding Tasks with AI

If you have been coding for more than a few years, you know the feeling. You are in the zone, solving a complex architectural problem, when suddenly you hit a wall of boilerplate. Maybe it is writing the same CRUD API endpoints for the tenth time, manually formatting a JSON file, or writing extensive unit tests for a simple utility function.

This is “Toil”, repetitive, low-value work that scales linearly with service growth but adds little creative value.

In 2025, the definition of a “productive developer” shifted. It is no longer about how fast you can type syntax; it is about how effectively you can orchestrate AI and developer tools to remove that toil. Let’s explore how to automate the boring stuff so you can focus on the logic that matters.

---

The Three Pillars of Modern Automation

Automation in development isn’t just one tool; it’s a stack. We can categorise the current landscape into three pillars:

1. The “Sidekicks” (AI Autocomplete): GitHub Copilot, Cursor, Supermaven.
2. The “Architects” (LLMs): ChatGPT (GPT-4), Claude 3.5 Sonnet, Gemini.
3. The “Deterministics” (Linters/Formatters): Prettier, ESLint, Husky.

The magic happens when you chain these together.

---

Five Practical Use Cases for AI Automation

Here is how you can move from theory to practice immediately.

1. Banishing Boilerplate

Starting a new component or microservice often involves 20% creative thought and 80% typing standard configurations.

• The Old Way: Copy-pasting from an old project and carefully finding/replacing variable names.
• The AI Way: Use a context-aware editor like Cursor or Copilot. Prompt: “Create a React functional component for a ‘UserProfile’ card using Tailwind CSS, including props for avatar, name, and bio, with a skeleton loading state.”

---

2. Writing Unit Tests (The Task Everyone Procrastinates)

We all know we should have 100% coverage, but writing assertions for edge cases is tedious. AI excels here because unit tests are highly pattern-based.

• Strategy: Don’t just ask AI to “write tests.” Give it the logic.
• Workflow: Highlight your function and ask: “Generate Jest tests for this function. Include edge cases for null inputs and large arrays.”
• Pro Tip: AI often hallucinates imports. Always run the tests immediately to verify.

3. The “Regex & SQL” Translator

Very few developers have complex Regex patterns or obscure SQL window functions memorized. Instead of spending 20 minutes on StackOverflow, treat AI as a translator.

• Input: “Write a Regex to match a password that needs 1 uppercase, 1 special char, and is 8-20 chars long.”
• Output: ^(?=.*[A-Z])(?=.*[!@#$&*])(?=.*[0-9]).{8,20}$ (Plus an explanation of how it works).

4. Generating Documentation from Code

Stale documentation is the silent killer of productivity.

• Tooling: Tools like Mintlify or IDE extensions can scan your codebase and auto-generate docstrings or README files.
• The Benefit: You ensure that the documentation actually matches the current state of the code.

---

5. “Meta-Automation” (AI Writing Scripts)

This is the highest leverage move. Instead of doing a task manually, ask AI to write a Python or Bash script to do it for you.

Scenario: You have 500 images that need to be resized and converted to WebP format.

• Don’t: Open Photoshop.
• Do: Ask Claude/ChatGPT: “Write a Python script using the Pillow library to look at the /assets folder, resize all images to 1920px width, convert them to WebP, and save them in a /dist folder.”

---

The “Human in the Loop” Guardrails

Automation is powerful, but it is not autonomous. To avoid merging garbage code, adhere to these rules:

• Trust, but Verify: AI is a probabilistic text generator, not a logic engine. It can write code that looks correct but fails silently. Never skip code review just because AI wrote it.
• Security First: Be wary of pasting API keys, proprietary algorithms, or PII (Personally Identifiable Information) into public LLM interfaces. Use enterprise instances where data privacy is guaranteed.
• Context is King: AI struggles with “spooky action at a distance.” If you ask it to refactor a file, it might break a dependency in a different folder unless your AI tool has context awareness of the whole repository.

---

Conclusion: The New Developer Workflow

The goal of automating repetitive tasks isn’t to code less; it’s to deliver value faster.

By offloading the “toil” to AI and scripts, you free up your mental RAM for the hard stuff: system design, business logic, and user experience. The developers who thrive in the next decade won’t just be great coders; they will be great editors and orchestrators of AI tools.

Start small. Pick one task you hate doing today (like writing commit messages) and find a tool to automate it.