Forem: Daria Tsion

I built an Interview Agent and would love your feedback ^^

Daria Tsion — Thu, 22 Jan 2026 14:24:54 +0000

Problem: QA interviews don’t reflect real QA work

Most QA interviews still focus on:

generic theory questions
memorized definitions
the same question list for every project

As a QA Lead, I kept running into the same problems:

strong candidates failing interviews because questions were too abstract
weaker candidates passing by answering theory well
interviewers improvising instead of following a clear structure
important project risks never being discussed

The biggest gap:

interviews rarely reflect how QA will actually work on this specific product.

Agent welcome / intro screen

Solution: a QA Interview Agent that starts with analysis, not questions

I built an AI QA Interview & Evaluation Assistant to support structured, evidence-based interview preparation.

This agent is not meant to:

replace interviewers
make hiring decisions
auto-score candidates

Instead, it helps QA Leads and Hiring Managers:

analyze role + project requirements
identify real risks and weak spots
turn that context into a clear interview plan

Agent instructions / role definition

How the agent works (step by step)

1. It collects real context, not just a role title

The agent asks for:

candidate CV
project domain and business logic
must-have vs nice-to-have requirements
team setup and constraints
release pace and risk level

If information is missing, it:

asks concise clarifying questions
or proceeds with clearly stated assumptions

Clarifying questions / inputs

2. It analyzes before generating anything

Before writing interview questions, the agent:

summarizes the candidate’s background
highlights potential risks and gaps
maps project requirements to real QA responsibilities

Examples:

complex calculations → edge cases, rounding, VAT
manual-only team → regression ownership
high financial impact → escalation and documentation

CV summary + risks / gaps analysis

3. It builds a structured interview plan

Instead of a flat list, the output is a full interview plan:

30–50 questions (configurable)
grouped by real QA responsibility areas
aligned with project risks and role expectations

Typical sections:

QA fundamentals & mindset
Test design & edge cases
Regression & release testing
Documentation & requirements analysis
Data & calculations validation
Collaboration & ownership
Scenario-based problem solving

Interview plan structure

4. Every question is intentional

Each question includes:

what competency it checks
what a Middle-level signal looks like
a follow-up probe for deeper validation

This helps interviewers:

stay consistent
reduce subjectivity
adapt depth based on candidate answers

5. Optional: table format for live interviews

The final interview plan can also be generated as a table, ready to use during the interview:

Category
Question
Competency checked
Middle-level signal
Follow-up probe

This works well for:

panel interviews
shared interview docs
interview calibration

Table view

Why this approach worked better for me

Using this structure helped me:

focus interviews on real product risks
ask fewer but higher-quality questions
feel more confident in decisions
spot gaps that generic interviews missed

Ask for feedback 🙌

This agent is still in the testing and iteration phase.

I’d love feedback from:

QA Leads
Hiring Managers
anyone who conducts technical interviews

If you’re curious:

try it
skim the structure
tell me what feels useful and what doesn’t

Your feedback will directly influence the next iteration.

Link here: QA Interview Questions Generator

👋 Hi dev.to! I'm QA Lead / QA Manager working with QA leadership & AI in testing. Open to interviews, podcasts, and written features on QA & mentoring. Feel free to reach me out!

Daria Tsion — Tue, 16 Dec 2025 17:12:56 +0000

How AI Makes Us Lazy Thinkers — and My Own 3 Ways to Stay Sharp

Daria Tsion — Thu, 20 Nov 2025 17:22:00 +0000

I’ve noticed something lately: we’re getting lazy at thinking.
AI has become so convenient that instead of saying “I’ll think about it,” we just say “I’ll ask ChatGPT.”
The problem isn’t using AI — it’s that we stop forming thoughts clearly. Our prompts are getting shorter, vaguer, as if we expect the machine to just guess what we mean.

But thinking is like a muscle — if you don’t train it, it weakens.

3 Ways to Keep Your Thinking Skills Alive in the AI Era

Write prompts as if you’re talking to a human

A prompt reflects your thinking. If it’s vague, your thought probably is too.
🔹 Try giving AI the same context and clarity you’d give to a teammate: what’s the goal, what’s the background, what examples help?
🔹 This keeps your mind in “structured thinking mode” — not autopilot.

Don’t delegate thinking. Delegate execution.

Think first — then ask AI

AI gives answers instantly, and your brain loves that shortcut. But growth happens when you form your own idea first.
🔹 Take 2 minutes to come up with your own version or hypothesis before you ask AI.
🔹 Then compare and analyze the difference.

Don’t let AI be your first brain. Let it be your second one.

Analyze — don’t just copy

Copying responses is quick but empty.
🔹 Always ask: “Why does this make sense?” or “Would this work in my context?”
🔹 Break down the logic behind the answer — that’s where the real learning happens.

Stay the thinker, not just the user.

✨Final thoughts:

AI won’t make us dumber — unless we let it 😅.
Because intelligence isn’t about knowing the answer.
It’s about being curious enough to ask the right question.

When to Automate and When to Test Manually: A QA Leader’s Decision Framework

Daria Tsion — Wed, 29 Oct 2025 20:15:16 +0000

“Not everything that can be automated should be.”

As QA engineers and leads, we often face the same question:

Should we automate this, or handle it manually?

And the truth is — there’s no single answer.

The right choice depends on context, stability, and value.

Automation is powerful when it accelerates learning and confidence, not just when it replaces human effort.

But manual testing is equally valuable when it provides context, empathy, and deep insights automation can’t reach.

After leading hybrid teams of manual and automation testers, I built a simple framework to guide these decisions.

🧩 My “Should We Automate It?” Decision Matrix

When we’re unsure whether to automate a scenario, my team runs through this quick self-check.

If most answers are “Yes” — we automate.

If “No” dominates — it stays manual until it stabilizes or proves recurring value.

💡 Question to Ask	YES → Lean Towards Automation	NO → Keep Manual for Now
Is this scenario repeated often (e.g., regression, smoke tests)?	✅ It’s worth automating — it’ll save time long-term.	🚫 It’s a one-off or rare flow; manual testing is fine.
Is the feature stable (logic and UI rarely change)?	✅ Good candidate — stable behavior means low maintenance.	🚫 Frequent changes = automation debt.
Do we have clear acceptance criteria or expected results?	✅ Perfect — automation thrives on predictability.	🚫 Too ambiguous? Manual exploration will find more insights.
Would automation provide faster feedback than manual testing?	✅ Yes — prioritize it for quick CI/CD validation.	🚫 No — setup might take longer than manual checks.
Can this be reliably automated with existing tools?	✅ Great — low technical complexity.	🚫 Hard to automate (CAPTCHA, payments, animations) — stay manual.
Will it increase confidence before releases?	✅ Valuable regression safety net.	🚫 Not critical to overall quality confidence.
Do we have ownership and capacity to maintain it?	✅ Go for it — sustainable automation.	🚫 If not maintained, automation adds risk, not value.

🧩 Pro tip:

If you answer Yes to 4 or more — it’s a good automation candidate.

If less than 4 — keep it manual, monitor its stability, and revisit later.

🧱 The Testing Pyramid: Finding the Right Balance

Another concept that influences automation decisions is the Testing Pyramid.

It reminds us that not all automated tests are equal — some are fast and reliable, while others are slow and costly.

At the base, we have unit tests and static analysis — fast, cheap, and easy to automate.

As we move up to integration and end-to-end (E2E) tests, the cost and maintenance effort increase significantly.

E2E automation is valuable, but it’s also:

🐢 Slower — involves UI, databases, and APIs.
🧩 Fragile — small changes can break multiple tests.
💸 Expensive — requires setup, infrastructure, and constant debugging.

That’s why teams need a balance — Automate stable, repetitive flows, and keep manual or exploratory testing for learning, usability, and high-risk areas.

Sometimes, one well-structured manual session provides more insight than ten brittle automated tests.

🧠 Example from Real QA Practice

One of the most controversial areas we deal with is Billing and Plans.

It’s a feature that’s both business-critical and highly volatile — prices, currencies, and plan configurations change often, sometimes even daily.

From a risk perspective, this makes it one of the hardest modules to test:

Frequent updates cause constant UI and API changes.
Logic for upgrades, downgrades, price changes, and trials has multiple dependencies.
And yet, every release depends on it being correct — even a small bug can immediately affect real users and revenue.

So, according to our decision matrix, Billing would normally look like a “manual-first” area:

unstable, complex, and expensive to maintain in automation.

But in reality, we made the opposite choice.

We prioritized it for automation despite instability, because the business risk outweighed the maintenance cost.

We focused on:

Automating core payment paths (subscription start, renewal, downgrade).
Using AI-assisted regression prompts to detect pricing inconsistencies early.
Keeping edge cases manual for flexibility and context.

This hybrid approach allows us to:

React fast to business logic changes.
Maintain confidence in daily releases.
Still learn manually where automation can’t keep up.

So even when the matrix says “manual,” the context may say “critical — automate anyway.”

That’s why frameworks should guide us, but not replace human judgment.

⚖️ Visual Summary

Testing Pyramid Reminder

Base → Fast & Stable (Unit, Integration) → Automate.
Top → Slow & Costly (E2E, Exploratory) → Balance with Manual.

Automate when: stable, repeatable, measurable.

Stay manual when: learning, exploring, or unstable.

Ask first: “Will automation bring clarity — or complexity?”

✅ Key Takeaways

Don’t automate everything — automate what brings clarity and speed.
Manual testing isn’t “less valuable” — it’s how we explore and learn.
Use the decision matrix to evaluate value vs. effort.
E2E tests are powerful but costly — balance them with faster layers.
Revisit automation decisions often; context changes over time.

💬 How do you decide when to automate?

I’d love to hear how your teams draw the line between automation and manual testing — do you use a framework or rely on instinct?

AI Skills Every QA Should Learn in 2026

Daria Tsion — Thu, 23 Oct 2025 22:02:21 +0000

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.

6 Mistakes That Made Me a Better Leader (I hope 😅)

Daria Tsion — Thu, 23 Oct 2025 19:34:11 +0000

When I officially became a team lead, I thought leadership meant having all the answers, making all the decisions, and fixing every problem myself.
Spoiler: it doesn’t 😅

What actually shaped me into a better leader were the mistakes I made — the moments that forced me to slow down, reflect, and change how I work with people.

Here are six lessons that came from real missteps:

🧠 1. Trying to do everything myself

When I started leading, I thought being a “good lead” meant handling everything — planning, testing, reporting, even small fixes.
I believed my team’s success depended on my effort.

But all I did was burn myself out and slow everyone down.

✅ Lesson: Delegation is not a weakness — it’s trust.
Your team grows when they own decisions. You grow when you let them.

💬 2. Avoiding difficult conversations

There were moments when I saw issues — misalignment, low performance, missed expectations — but I stayed silent to “keep the good mood” because I haaaate complaining about the people and making them feel incorrect. But...

That silence cost more than any awkward talk ever would.

✅ Lesson: Honest feedback is care, not conflict.
Tough conversations build clarity and respect — avoiding them builds frustration.

📊 3. Measuring output, not impact

At one point, I was obsessed with numbers:
How many test cases? How many bugs? How many tickets closed?

But quantity isn’t about the quality.

✅ Lesson: A leader should measure impact, not just output.
Did our work make the product better? Did it help users? Did it reduce risk?
Those are the questions that matter.

👑 4. Thinking leadership is about titles

When I got my first “lead” title, I felt pressure to act the part — to always be the one deciding, speaking, guiding.
It took time to realize that leadership isn’t something you get promoted into.

✅ Lesson: Leadership is about influence, not position.
Anyone can lead from any seat if they inspire trust and take responsibility.

🎯 5. Overcommunicating tasks, undercommunicating vision

Early on, I talked a lot about tasks — what to do, when to do it, how to do it.
But I rarely talked about why it mattered.

✅ Lesson: Teams don’t get motivated by to-do lists — they get motivated by purpose.
When people understand the “why,” they’ll figure out the “how.”

🎉 6. Forgetting to celebrate small wins

We were always chasing the next milestone, the next release, the next sprint.
But we rarely paused to appreciate what we’d already achieved.

✅ Lesson: Celebration builds culture.
Recognizing small wins reminds the team (and yourself) that progress matters — even if it’s not perfect.

🤘 Final Thoughts

Being a leader isn’t about avoiding mistakes.
It’s about learning faster than you repeat them.

Every wrong decision, every awkward feedback session, every misjudged moment — they’re all part of your growth story.

Leadership isn’t perfection.
It’s progress, reflection, and a lot of humility.

💭 What mistake taught you the most as a leader?
Drop it in the comments and let’s learn from each other.

No Bugs, Only Bats — My DEV Haunted Hub for Halloween 🦇

Daria Tsion — Tue, 21 Oct 2025 17:59:25 +0000

This is a submission for Frontend Challenge - Halloween Edition, Perfect Landing

🕸️ What I Built

I built a dark-themed Halloween landing page called “DEV Haunted Hub,” inspired by the DEV Community.

It’s a mix of spooky CSS, smooth animations, and DEV flair — combining a haunted aesthetic with a community-driven theme.

🎃 Demo

🧙‍♀️ Journey

I wanted to create something fun and visually appealing that still feels like part of the DEV ecosystem.

The idea came from combining Halloween vibes (bats, pumpkins, and glowing gradients) with the recognizable DEV look.

I focused on accessibility, CSS reusability, and responsiveness — the layout adapts cleanly from desktop to mobile.

This project reminded me how much creativity and polish can come from “just CSS and HTML.”

Special thanks to the DEV team for inspiring community-led creativity every season!

👩‍💻 About Me

Created by Daria Tsion — QA Lead and AI Testing Enthusiast.

Follow me for insights on testing, QA automation, and creative frontend experiments.

Happy Halloween and good luck to everyone joining the challenge! 🧡

🧭 From QA Engineer to Mentor: What I’ve Learned Through ADPList

Daria Tsion — Mon, 20 Oct 2025 18:25:38 +0000

When I joined ADPList as a mentor, I didn’t expect how much I’d learn from my mentees.

Over first sessions, I’ve talked to developers, automation engineers, and other leaders from all over the world — each one bringing unique challenges and stories.

Here are a few lessons this journey has taught me 👇

1️⃣ Mentorship is two-way learning

Every conversation sharpens my perspective.
Explaining test strategy, AI use in QA, or automation frameworks often helps me articulate ideas better and refine my own leadership mindset.

2️⃣ Growth starts with clarity, not tools

Many mentees ask about QA Leadership or General testing — but we always start with “why.”
Understanding what problem you’re solving is more valuable than any framework.

3️⃣ Empathy builds better QA teams

Good mentoring (like good QA) is about curiosity, patience, and context.
Listening deeply often reveals the real blocker — confidence, not code.

💬 Why I Mentor

Because I believe sharing knowledge multiplies it.
Mentorship makes our QA community stronger, more connected, and ready for AI-driven change.

If you’re a QA professional looking to grow — or want to give back — ADPList is an incredible place to start.

👩‍🏫 You can book a free session with me here

🚀 And if you want to explore AI in QA, join me at AI & QA Leaders

🧠 Want to write about AI in Testing? Join AI & QA Leaders!

Daria Tsion — Sat, 11 Oct 2025 19:40:24 +0000

I'm inviting QA engineers, AI testers, and automation enthusiasts to publish under the AI & QA Leaders organization.

💡 If you’d like to contribute, just comment below with your dev.to username (or DM me) — and I’ll send you an invite!

Let’s build a space for smarter, data-driven QA together 🚀
🔗 Learn more about us: AI & QA Leaders

When QA Meets AI 😅. Launching the AI & QA Leaders Community

Daria Tsion — Fri, 10 Oct 2025 23:47:27 +0000

This is a submission for the 2025 Hacktoberfest Writing Challenge: Maintainer Spotlight

A year ago, I started noticing a shift in QA where testers weren’t just finding bugs anymore. They were learning to collaborate with AI.

After over a decade in QA and leadership, I realized we needed a place to share this evolution — somewhere testers could learn, experiment, and lead the change together.

AI is no longer a futuristic concept — it’s becoming every tester’s daily teammate 🤖✨

That’s how AI & QA Leaders was born — a dev.to hub for QA engineers passionate about AI, automation, and smart testing.

💡 What we share:

Real-world AI use cases for testing and automation
QA leadership and productivity insights
Tutorials, frameworks, and success stories from QA teams worldwide
Open invitations for contributors and mentors

🤝 How you can contribute:

Publish your own article under AI & QA Leaders
Share your experience with AI in testing
Help others grow through collaboration and learning 👉 Join us in shaping the next era of QA innovation: dev.to/qa-leaders.

Because the future of QA isn’t just automated — it’s intelligent.

🤖 AI as Your QA Pair Buddy

Daria Tsion — Thu, 09 Oct 2025 17:49:43 +0000

When people talk about “AI in testing,” they usually imagine some fancy automation tools or AI-generated test cases.
But here’s the truth: I use AI completely differently.

For me, AI has become my QA pair buddy — someone I brainstorm with, test ideas with, and even argue with.
Not to replace my thinking, but to challenge it.

💭 How It Started

I used to spend hours reviewing release notes, thinking:

What did we really change here?
Where’s the highest risk?
What could possibly break that we’re not seeing yet?

Now, instead of overthinking alone, I open ChatGPT and say:

“You’re my QA teammate. Help me find hidden risks in these release notes.”

And you know what? It often points out things I might’ve missed — not because it’s smarter, but because it forces me to look again from a different angle.

⚙️ What My “AI Pair Buddy” Does

Here are a few ways I use AI daily in my QA routine:

1️⃣ Brainstorming edge cases

“Act as QA reviewing a checkout feature. What scenarios might fail under heavy load or poor internet?”

2️⃣ Clarifying unclear requirements

“Rephrase this user story from a QA perspective — what’s missing or ambiguous?”

3️⃣ Pre-reviewing regression risks

“Analyze this changelog and tell me which areas of the product are most likely to be impacted.”

4️⃣ Helping with test documentation

“Summarize this long testing session into 3 bullet points I can paste into Jira.”

These are not “automations.” They’re collaborations.

🧠 Why It Works

AI doesn’t get tired.
It doesn’t assume things.
And it doesn’t take offense when you ask “stupid” questions.

So, instead of working for me, it works with me.
It helps me test my logic before I test the code.

That’s what I call AI-assisted critical thinking — not letting AI decide, but letting it provoke new questions.

⚡ The Shift That Changed Everything

At some point, I stopped thinking of AI as a chatbot and started treating it as a junior teammate.
Someone who can:

brainstorm freely,
make mistakes safely,
and push me to think differently.

The best part?
It made my testing faster, more creative, and way more fun.

🎯 Final Thought

The future of QA isn’t about replacing testers with AI.
It’s about giving every tester a thinking partner — one that’s available 24/7, asks the hard questions, and keeps your curiosity alive.

If you treat AI like your pair tester instead of a magic box,
you’ll realize it’s not automation that’s powerful — it’s collaboration.

How do you use AI in your QA work today? I’d love to hear your thoughts below 👇

💡Follow AI & QA Leaders for more insights on AI in testing, automation, and QA leadership.

How I Use AI in QA: From Prompts to Partnering

Daria Tsion — Tue, 07 Oct 2025 23:07:06 +0000

“The better we ask, the better we test.”

AI is changing how QA engineers work — not by replacing us, but by extending what we can do.

As a QA Lead working with automation teams, I’ve spent months experimenting with AI tools like ChatGPT and Claude for real QA workflows — from generating Cypress tests to debugging flaky suites.

What I learned is simple: prompt engineering is the new test design.

💡 What’s “Advanced Prompting” in QA?

Basic prompting is just asking a question and hoping for a good answer.

Advanced prompting is when you teach the AI to think like a QA engineer — by adding roles, context, examples, and constraints.

It’s like writing acceptance criteria for your AI.

⚙️ Real Scenarios Where AI Helps in Automation QA

Here’s how I now use AI in my daily QA work:

Generate maintainable test scripts in Cypress or Playwright
Diagnose flaky tests and suggest refactors
Create synthetic test data with realistic edge cases
Summarize regression results for weekly reports
Refactor old automation code for readability

Let’s look at a few examples 👇

🧠 My Advanced Prompt Framework

Every prompt I use now follows the same pattern:

Role → Context → Constraints → Output Format

For example:

Act as a senior QA automation engineer.
Based on the acceptance criteria below, generate Cypress tests using the Page Object Model.
Include before/after hooks, positive + negative flows, and clear assertions.
Output: code blocks with file paths and brief comments.

Acceptance criteria:
User can log in using email and password. Invalid credentials should show an error message.

This small structure makes a huge difference in how AI interprets your intent.

🔁 Before vs After

Simple prompt:

“Write Cypress tests for login.”

🧩 Result: 1–2 generic tests, no structure, hardcoded selectors.

Advanced prompt:

“As a QA automation engineer testing a SaaS app with MFA login, write 5 Cypress tests using data-cy selectors and intercepts.

Include both valid and invalid flows, and verify API + UI responses.”

✅ Result:

Structured Page Object tests, reusable code, coverage for both UI and API layers.

🧩 Real Example — Diagnosing Flaky Tests

Prompt:

Here’s a failing Cypress test log.
Identify likely root causes (timing, async, selectors).
Propose fixes and refactor the test for reliability.

AI output:

Found that waits were inconsistent (cy.wait() instead of cy.intercept())
Suggested refactoring selectors to data-cy
Recommended isolating login flow from other suites

We applied it — and flakiness dropped by 40% in the next sprint.

📘 My QA Prompt Library

To make this process repeatable, I built a public repo:

👉 QA Advanced Prompting

It includes:

10+ tested prompts for automation QA
Examples for Cypress, Playwright, and API testing
A few ready-to-copy JSONs for Postman
Templates for AI-based code reviews

Feel free to fork it, test it, and contribute your own prompts.

🧩 Why It Matters

QA is evolving — and prompt engineering is part of that evolution.

It’s no longer just about finding bugs; it’s about designing intelligent test systems that learn, adapt, and assist.

“Prompting is not about tricking AI — it’s about teaching it to reason like a tester.”

If you’ve ever debugged flaky tests at 2 AM, you’ll appreciate having an AI co-tester.

💬 Key Takeaways

Advanced prompting = structured thinking. Treat prompts like test cases.
Give context. Framework, domain, and constraints matter.
Iterate. Refine prompts the same way you refine tests.
Reuse. Keep your best prompts in version control.

🚀 Ready to Try?

🧩 Explore the repo: QA Advanced Prompting on GitHub

🧠 Start building your own “AI QA Library” — it’ll become your best automation buddy.

Authored by Dasha Tsion — QA Lead, automation enthusiast, and believer that AI should work with us, not instead of us.

This post is a short reflection based on my upcoming article for Ministry of Testing.