Olha Qoolli

04/06/2026

Payment System Testing Checklist

1. Positive Scenarios
2. Refunds and Cancellations
3. Wallet Payments
4. Recurring Payments
5. Security
6. Multi-currency Support
7. Negative Scenarios (Card Data)
8. Negative Scenarios (Card / Account Status)
9. Network Failures
10. Data Integrity
11. Integration Testing

31/05/2026

Are you using AI tools in your business?
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Are you happy with the results, or do they sometimes miss the mark?
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An AI giving a “weird reply in chat” is only half the problem.
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It can get much worse…
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😱 complete deletion of company data
😱 leaks of confidential information
😱 critical decisions based on false data
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And that hits not only the budget, but the company’s reputation too.
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That’s exactly why market leaders are investing not only in AI development, but also in testing, quality control, and security.
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Scenario testing, monitoring model behavior, finding vulnerabilities, controlling hallucinations — all of this is gradually becoming as normal as QA in traditional software development.
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AI is an incredibly powerful tool. But without systematic quality control, it quickly turns from an advantage into a source of risk.
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This is what we teach people to do: stand between AI and the market — control quality, verify results, and build reliable processes.
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So AI works for you, not against you.
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And what about your company? Are your AI tools already going through proper testing, or are they still running mostly on trust?
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24/05/2026

Harmless AI hallucinations? That era is over.

4 real cases where AI agents crossed the line:

• PocketOS (2026) — an AI found an admin token and deleted a database along with 3 months of backups.
• Replit (2025) — an AI deleted data from 1,200 executives while insisting everything was recoverable.
• McKinsey Lilli (2026) — an autonomous AI gained access to 46.5M messages through an old API vulnerability.
• OpenAI / Palisade Research (2025–2026) — models resisted shutdown, bypassed oversight, and hid capabilities to keep working.

The takeaway is simple: AI agents no longer just generate text. They can access code, servers, and data — and make risky decisions without human approval.

The question isn’t whether you use AI.
The question is: are you testing its security, or simply hoping for the best? #ольгааркуша

16/05/2026

Before, AI safety was mostly a matter of ethics and the goodwill of developers. But that is about to change in a very real way…
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The European Union has adopted the EU AI Act, which effectively moves AI testing from the “optional” category into a legal obligation. Ignoring the rules can be expensive — fines of up to €35 million or 7% of a company’s global turnover.
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What does this mean for business?
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If you plan to bring AI products to the EU market, be ready for the law to require structured model testing processes, including:
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1. Adversarial Testing & Red Teaming: serious checks for resilience against attacks and attempts to extract confidential data.
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2. Bias & Fairness Audits: reducing and preventing discrimination in algorithms.
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3. Vulnerability Assessments: protection against data poisoning and prompt manipulation.
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4. Capability Evaluations: assessing hidden model capabilities and dual-use risks.
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The winners will be those who not only know how to build AI, but also know how to make it safe, reliable, and compliant with EU requirements!
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The market is only entering this phase now. That’s why it already makes sense to connect with teams and specialists who are genuinely focused on standardization, compliance approaches, and AI system testing.
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On our side, we are actively studying this space, building hands-on experience, training specialists, and speaking with companies that want to navigate this transition calmly and without unnecessary losses.
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If you work with AI and understand that legal review is only a matter of time — feel free to message me. I’d be glad to discuss the market’s real challenges and how we might be useful to each other in this new reality.
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09/05/2026

Imagine this: one neural network answers a question, and another one checks how good that answer is.
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That’s what LLM-as-a-judge means — a way to evaluate one AI model’s answers using another AI model.
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Example:
You ask: “Why is the sky blue?”
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Model A gives an answer, and Model B reads it and says: “good enough” or “not great.”
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Sometimes a person gives Model A two options and asks, “Which is better: A or B?” Then Model B evaluates Model A’s choice.
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Why is this useful?
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Checking AI answers manually takes time and costs money, so another neural network is used as a “judge.”
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But there’s a catch!
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The judge model doesn’t always know what’s true — it may choose the more “beautiful” answer even if it’s wrong. It also tends to like longer texts (even when they’re worse).
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Remember the key point:
A judge model is good at understanding:
✅ what sounds logical
✅ what looks like a strong answer
But it’s worse at understanding: what is actually true ❗
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Bottom line:
LLM-as-a-judge is a fast way to evaluate AI responses, but it still can’t fully replace humans. Yes, yes — testers are still needed.
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Are you already using automated response evaluation in your projects, or do you still prefer good old manual quality control?
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28/04/2026

Building your own LLM without serious testing is like launching a rocket and hoping for the best.
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According to the latest Galileo report (Q1 2026), 85% of teams have faced at least one AI incident in the past 6 months.
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Do you know what the most dangerous trap is?
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Overconfidence!
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Companies that think their scenarios are “safe” end up with 11% more issues than those who honestly admit they didn’t have enough time for testing.
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Why did the market leaders (elite teams) become leaders?
Because they take testing seriously.
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They cover 90–100% of AI behavior with tests.
They spend over 40% of development time on evaluation (evals).
The result: their solutions are 2.2× more reliable than the market average.
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It’s time for businesses to accept a simple truth: if your AI is “silent” about errors, it usually says more about poor diagnostics than actual quality.
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Real reliability isn’t about having no bugs. It’s about a system being able to detect them before your users do.
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Testing isn’t a boring report at the end of the quarter. It’s the only way to turn an expensive toy into a real business tool.
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Honestly – would you trust your product to a model that isn’t tested systematically?
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28/04/2026

Building your own LLM without serious testing is like launching a rocket and hoping for the best.
‌
According to the latest Galileo report (Q1 2026), 85% of teams have faced at least one AI incident in the past 6 months.
‌
Do you know what the most dangerous trap is?
‌
Overconfidence!
‌
Companies that think their scenarios are “safe” end up with 11% more issues than those who honestly admit they didn’t have enough time for testing.
‌
Why did the market leaders (elite teams) become leaders?
Because they take testing seriously.
‌
They cover 90–100% of AI behavior with tests.
They spend over 40% of development time on evaluation (evals).
The result: their solutions are 2.2× more reliable than the market average.
‌
It’s time for businesses to accept a simple truth: if your AI is “silent” about errors, it usually says more about poor diagnostics than actual quality.
‌
Real reliability isn’t about having no bugs. It’s about a system being able to detect them before your users do.
‌
Testing isn’t a boring report at the end of the quarter. It’s the only way to turn an expensive toy into a real business tool.
‌
Honestly – would you trust your product to a model that isn’t tested systematically?
‌

20/04/2026

Course Program. What’s inside?

Module 1️⃣ Testing Theory. Defect Lifecycle (DLC)
Verification vs validation, testing goals.
Terminology, levels, types, ISTQB principles.
Bug detection and clear bug reports.
Exploratory testing and test tours.
LinkedIn profile setup for IT presence.

Module 2️⃣ Test Design. Working with AI Tools. Checklist
Product analysis, risk-based approach, test generation.
Creating business checklists with AI.
Black-box techniques and equivalence partitioning.
Boundary Value Analysis for critical defects.

Module 3️⃣ Web Testing. Test Plan
Creating a Test Plan and understanding STLC.
Cross-browser and mobile testing.
Using DevTools and simulators for bug detection.
Basics of SDLC and QA role.
Intro to HTML/CSS, JSON/XML.

Module 4️⃣ Mobile App Testing. Test Case
Platforms and app types, installation basics.
Working with builds and testing stages.
Pre-release testing.
Writing test cases and scenarios.
Checklist testing and recording defects.

Module 5️⃣ Performance Testing. Test Suite
Types of performance testing.
Client-server basics, HTTP requests.
JMeter setup and usage.
Load scenarios, metrics, reporting.
Java installation, CMD/Terminal basics.

Module 6️⃣ Security Testing. API Testing. Collections
Cybersecurity basics and vulnerabilities.
QA strategies for secure systems.
Checklist testing for security.
Postman, Swagger for API testing.
MySQL and SQL basics.

Module 7️⃣ Game Testing
Game testing approaches and industry specifics.
Key QA skills and requirements.
Compatibility across devices.
Testing gameplay, UI, localization.
Clear bug reporting with visuals.

Module 8️⃣ AI Application Testing
AI vs traditional testing.
Testing CV, NLP, generative systems.
Testing LLMs without ground truth.
AI quality metrics (e.g., F1 Score).
QA role as analyst and UX researcher.

Module 9️⃣ Automated Testing: Selenium / Java / Git
Purpose of automation.
Selenium WebDriver basics.
Java for test scripts.
Creating and running tests.
Git basics: commits and branches.

Module 🔟 Working in an IT Company. Agile
Agile principles and iterations.
Scrum: User Stories, Backlog, sprints.
Daily Scrum and task boards.
Sprint Retrospectives for improvement.

20/04/2026

20/04/2026

Course Program. What’s inside?
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Module 1️⃣ Testing Theory. Defect Lifecycle (DLC)
‌
Verification vs validation, testing goals.
Terminology, levels, types, and the 7 ISTQB principles.
Bug detection and writing clear, effective bug reports.
Exploratory testing and test tours without documentation.
Setting up a LinkedIn profile as a foundation for your IT reputation.
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Module 2️⃣ Test Design. Working with AI Tools. Checklist
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Product analysis, risk-based approach, and test generation.
Fast creation of business-oriented checklists using AI.
Black-box techniques and equivalence partitioning to save time.
Boundary Value Analysis (BVA) to catch critical defects.
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Module 3️⃣ Web Testing. Test Plan
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Creating a Test Plan and understanding the STLC cycle.
Cross-browser and mobile layout testing.
Bug detection using Browser DevTools and simulators.
Understanding SDLC and the role of a QA engineer in a team.
Reading HTML/CSS code and JSON/XML data formats.
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Module 4️⃣ Mobile App Testing. Test Case
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Types of platforms and apps, installation specifics.
Working with builds and stages of mobile testing.
Preparing and testing apps before store release.
Creating professional test cases and working with scenarios.
Checklist-based testing and video recording of defects.
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Module 5️⃣ Performance Testing. Test Suite
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Types of performance testing and how websites work.
Client-server architecture, HTTP requests, and responses.
Installing and configuring JMeter for performance testing.
Creating load scenarios, analyzing metrics, and reporting.
Installing Java and working with CMD/Terminal.
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Module 6️⃣ Security Testing. API Testing. Collections
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Cybersecurity basics and causes of web vulnerabilities.
QA strategies for building secure systems.
Checklist-based testing to prevent hacks and data leaks.
Working with Postman and Swagger: API requests and analysis.
MySQL databases and SQL for direct data validation.
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Module 7️⃣ Game Testing
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Game industry specifics and testing approaches.
Industry requirements and key Game QA skills.
Compatibility issues across platforms and devices.
Testing mechanics, levels, UI, and localization.
Writing clear bug reports with visual evidence.
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Module 8️⃣ AI Application Testing
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Differences between AI testing and traditional software testing.
Testing strategies for CV (vision), NLP (text), and generative systems.
Methods for testing LLMs without a ground truth.
Evaluating AI quality using metrics (e.g., F1 Score).
QA as an analyst and UX researcher in AI projects.
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Module 9️⃣ Automated Testing: Selenium / Java / Git
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Purpose and effectiveness of automated testing.
Working with Selenium WebDriver.
Java basics for writing reliable test scripts.
Creating, running, and analyzing automated tests.
Working with Git: commits and branch management.
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Module 🔟 Working in an IT Company. Agile
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Understanding Agile: flexibility, iterations, and feedback.
Scrum methodology: User Stories, Backlog, and sprints.
Daily Scrum practice and task visualization on a Scrum board.
Sprint Retrospectives for improving processes and efficiency.