How to Use AI to Generate Unit Tests (That Actually Work)

Unit testing is one of those tasks every developer knows they should do… but often avoid.
It’s repetitive, time-consuming, and not as exciting as building new features.

That’s where AI comes in. With the right prompts, you can generate reliable, context-aware unit tests in minutes — not hours.

Here’s how I use AI to make testing painless.

1️⃣ Turn Functions Into Test Cases

Instead of writing tests manually, let AI generate them directly from your function.

💡 Prompt Example:

“Write unit tests in Jest for this JavaScript function: [paste code]. Cover normal cases, edge cases, and error handling.”

Why: You get structured coverage instantly.

2️⃣ Cover Edge Cases Automatically

Developers often miss tricky scenarios. AI can expand coverage beyond the obvious.

💡 Prompt Example:

“Generate unit tests for this Python function. Include at least 3 edge cases I might overlook.”

Why: Increases robustness and prevents production bugs.

3️⃣ Generate Parameterised Tests

When functions take multiple inputs, AI can create parameterised tests that cover combinations.

💡 Prompt Example:

“Write parameterised unit tests in PyTest for this function with inputs [list inputs].”

Why: Saves time and ensures thorough testing.

4️⃣ Suggest Mocking Strategies

Complex apps need mocks for APIs, databases, or external services. AI can design them for you.

💡 Prompt Example:

“Create unit tests for this Node.js function. Mock the database call and verify return values.”

Why: It lets you test logic without depending on external systems.

5️⃣ Generate Documentation with Tests

AI can pair tests with clear documentation, showing why each case matters.

💡 Prompt Example:

“Write unit tests for this function and add a short explanation (1–2 sentences) for each test case.”

Why: Makes code reviews and future debugging easier.

Final Thought

Unit tests aren’t optional — they’re your safety net.
But with AI, they no longer need to be a burden.

By automating the first draft, you can focus on refining and validating, instead of slogging through repetitive boilerplate.

Resources for Smarter Testing

• Prompt Books → Ready-to-use libraries across business, authorship, productivity, and branding → ChatGPT Prompts Access
• My live lectures on prompts & productivity → ReThynk AI YouTube Channel
• Plug-and-play prompt systems (free & paid) → ReThynk AI Templates & Frameworks
• Professional AI, business, and tech insights (currently free on our website) → ReThynk AI Magazine

📌 Next Post: “Stop Writing Boilerplate Code: Let AI Do It for You” — a developer’s guide to automating repetitive code.

Comments

[Cyber Safety Zone]

Nice read, Jaideep — thanks for sharing this!

A few thoughts after going through your post:

• Using AI to "**bootstrap unit tests" **is a game changer. The ability to auto-generate edge cases or mock strategies cuts a lot of repetitive effort.
• I especially liked how you emphasize that AI shouldn’t replace human oversight — it’s about accelerating the first draft, then refining. That distinction is crucial in maintaining high test quality.
• One challenge I foresee: ensuring that the AI-generated tests stay maintainable as the code evolves. Maybe versioning test templates, or validating AI outputs as part of CI, could help.
• Also, for more complex systems (with side effects, async flows, integrations), AI might struggle a bit — so mixing human intuition + AI generation seems like a smart hybrid approach.

Overall this is exactly the kind of approach that helps developers spend less time on boilerplate and more time on logic. Looking forward to seeing follow-ups (e.g. applying this in large codebases or microservices).

[Jaideep Parashar]

Thank you @cyber8080! I completely agree, AI for bootstrapping unit tests is a time-saver, but human oversight is key to maintain quality. I’ve found that combining AI-generated tests with versioning and CI validation works well to keep them maintainable. And yes, for complex systems, human intuition is still crucial.

[Oscar]

I'm not against the idea of doing something like this, but writing tests by hand offers a few things that automating the creation of tests doesn't:

• You, the developer, get to inspect your code while you're writing tests. In the past, this has allowed me to pick up on bugs, possible improvements, or just generally weird things about something I made that probably shouldn't be that way.
• With AI generated tests (dare I say especially tests), you still need to go through the output and make sure everything is working correctly. If you're already going to put forth the brainpower to read an LLMs output, then why not just write the tests yourselves?

[Jaideep Parashar]

Absolutely, I see your point. Writing tests by hand does give a deep understanding of the code and helps catch subtle issues early. I view AI-generated tests as a starting point, a way to accelerate the first draft so developers can focus their brainpower on reviewing, refining, and improving the code. However, the human insight is still indispensable.

[Jaideep Parashar]

With the right prompts, you can generate reliable unit tests in minutes.

[Deepak Parashar]

True

[Roshan Sharma]

Great post! Using AI to auto-generate unit tests is super practical and can save a ton of time.

[Jaideep Parashar]

Yes true, Roshan.