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

[Jaideep Parashar]

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

[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).

[Deepak Parashar]

True