This content originally appeared on DEV Community and was authored by Daria Tsion
AI won’t replace QA — but QAs who use AI will replace those who don’t.
Why this matters
A few years ago, “AI in testing” sounded futuristic.
Now, it’s part of our daily routine — from generating test ideas to analyzing bug trends.
As a QA Lead, I see a clear difference between testers who leverage AI and those who ignore it.
The first group grows faster, communicates better, and spends less time on repetitive work.
And honestly — when we hire new QAs, I always pay attention to how candidates talk about AI.
If someone reacts negatively (“AI is useless”, “I don’t trust it”, “I just do things manually”), it’s a red flag 
It usually means they haven’t explored AI tools enough or they lack the critical thinking to evaluate them properly.
And without that mindset — it’s hard to progress in a modern QA team.
1. Prompting as a core QA skill
Prompting is basically asking better questions — but to AI.
Why it matters:
1. Generate test cases from user stories
2. Translate acceptance criteria into concrete scenarios
3. Explore “what if” or edge cases quickly
Example prompt:
“Act as a senior QA. Generate 10 edge-case test scenarios for a signup form with CAPTCHA and rate-limiting.”
Tip: Always use context. Don’t just ask “generate test cases” — tell the AI what environment, user type, and risks to consider.
2. AI for test documentation & reporting
Tools: Notion AI, Confluence AI, ChatGPT, Claude.
Use them to:
1. Convert messy notes into structured Test Reports
2. Summarize bugs and weekly QA updates
3. Draft retrospective summaries automatically
Example prompt:
“Summarize these Jira tickets into a weekly QA report with open/closed bugs and key blockers.”
You’ll spend less time formatting and more time analyzing what matters.
3. AI-assisted test design
Tools: ChatGPT, Mabl, Testim, Katalon AI.
Use AI to:
1. Generate test ideas from product requirements
2. Analyze risk areas
3. Review existing test plans for missing scenarios
Example prompt:
“Review this test plan for checkout flow and suggest 5 high-risk areas we might have missed.”
It’s not about outsourcing your brain — it’s about amplifying it.
4. Smart data generation
Tools: Mockaroo, ChatGPT (Code Interpreter), Synthesia.
AI can generate complex test data fast — CSVs, JSONs, user records, you name it.
Example prompt:
“Generate 200 fake user records with invalid email formats, duplicated IDs, and missing required fields.”
Perfect for boundary or negative testing.
5. AI for automation & debugging
Tools: GitHub Copilot, ChatGPT, Codeium, Testim AI.
Use it to:
1. Explain and refactor test automation scripts
2. Debug flaky tests
3. Learn new frameworks faster
Example prompt:
“Explain what this Cypress test does and suggest how to make selectors more stable.”
Even if you’re not a dev — AI can help you read and understand code more confidently.
6. Critical thinking — the most important skill
AI can make mistakes.
The real QA superpower is to question AI output:
- Does this make sense for my product?
- What assumptions did the model make?
- What’s missing?
Critical thinking is what separates a button pusher from a problem solver.
AI doesn’t replace judgment — it requires it.
Final Thoughts
The best QAs don’t fear AI — they learn how to collaborate with it.
Start small.
Try using AI for one report, one test plan, one data generation task a week.
Within a month, you’ll see real productivity gains — and you’ll start thinking differently about what’s possible.
The future of QA isn’t about tools — it’s about mindset.
AI won’t make you a great tester. But if you’re curious, analytical, and open-minded, it will make you unstoppable.
This content originally appeared on DEV Community and was authored by Daria Tsion