AI Skills for Software Testing

The Next Big Skill in QA: Testing Custom AI Models and GenAI Apps A massive shift is happening in Quality Assurance—and it’s happening fast. Companies everywhere are hiring QA Engineers who can test custom AI models, GenAI applications, and Agentic AI systems. New tools like: • Promptfoo (benchmarking LLM outputs) • LangTest (robust evaluation of AI models) • And techniques like Red Teaming (stress-testing AI vulnerabilities) are becoming must-haves in the QA toolkit. Why is this important? Traditional QA focused on functionality, UI, and performance. AI QA focuses on: • Hallucination Detection (wrong, fabricated outputs) • Prompt Injection Attacks (hacking through prompts) • Bias, Ethics, and Safety Testing (critical for real-world deployment) ⸻ A few real-world bugs we’re now testing for: • GenAI chatbot refuses service during peak hours due to unexpected token limits. • Agentic AI planner gets stuck in infinite loops when task chaining goes slightly off course. • Custom LLM fine-tuned on internal data leaks confidential information under adversarial prompting. ⸻ New Methodologies Emerging: • Scenario Simulation Testing: Stress-test AI agents in chaotic or adversarial conditions. • Output Robustness Benchmarking: Use tools like Promptfoo to validate quality across models. • Automated Red Teaming Pipelines: Constantly probe AI with bad actors’ mindsets. • Bias & Ethics Regression Suites: Identify when fine-tuning introduces unintended prejudices. ⸻ Prediction: In the next 12-18 months, thousands of new QA roles will be created for AI Quality Engineering. Companies will need specialists who know both AI behavior and software testing fundamentals. The future QA engineer won’t just ask “Does the app work?” They’ll ask: “Is the AI reliable, safe, ethical, and aligned?” Are you ready for the AI QA Revolution? Let’s build the future together. #QA #GenAI #AgenticAI #QualityEngineering #Promptfoo #LangTest #RedTeaming #AIQA

10 Skills Every SDET/QA Needs for the AI Era 🤖 Let's be honest: traditional QA skills aren't enough anymore. With AI and LLMs embedded in nearly every product, the role of QA is fundamentally changing. You're no longer just testing features—you're testing intelligence, reasoning, and behavior that shifts based on context. If you're not upskilling for AI-driven products, you're already behind. Here are the 10 critical skills you need to stay relevant: 1️⃣ LLM Fundamentals Understand tokenization, temperature, top-k/top-p sampling, embeddings, RAG basics, and model behavior. You can't test what you don't understand. 2️⃣ Prompt Testing Skills Validate output format, logical reasoning, consistency across runs, bias detection, and safety boundaries. Prompts are the new "test cases." 3️⃣ Hallucination & Groundedness Checks Detect factual errors, unsupported claims, missing citations, and fabricated information. LLMs are confident liars—your job is to catch them. 4️⃣ RAG Pipeline Testing Test the full flow: document ingestion → embeddings → retrieval → answer relevance. Weak retrieval = wrong answers, even with good models. 5️⃣ Agent Workflow QA Multi-step reasoning, tool calls, fallback logic, error recovery. AI agents are complex systems—test them like you would any mission-critical workflow. 6️⃣ AI Evaluation Frameworks Get hands-on with: LangSmith, Langfuse, Trulens, Ragas, Arize AI, DeepEval, Weights & Biases. These are your new test management tools. 7️⃣ API + Microservices Expertise GenAI apps are API-first architectures. Strong API testing isn't optional—it's foundational. 8️⃣ Scenario-Based Testing LLM behavior changes based on context. You need to validate end-to-end workflows, not just isolated inputs. 9️⃣ Adversarial & Safety Testing Jailbreak attempts, harmful content detection, refusal behavior, edge case adversarial prompts. If someone can break your AI, they will. 🔟 Data Quality & Drift Monitoring AI performance decays over time as data shifts. QA must track consistency, degradation, and model drift. 🚀 The Bottom Line: AI testing isn't traditional testing with AI tools bolted on. It's a completely new discipline that requires: ✅ Understanding how models work ✅ Knowing what "quality" means for non-deterministic systems ✅ Building evaluation frameworks that scale ✅ Thinking adversarial about failure modes The QA professionals who thrive in the next 5 years will be those who embrace this shift—not resist it. 💬 Let's Discuss: Which of these skills do you already have? Which one intimidates you the most? For me, adversarial testing was the hardest mindset shift—thinking like an attacker, not just a validator. Drop your thoughts below 👇 . . . #SDET #QA #AITesting #LLM #GenerativeAI #MachineLearning #QualityAssurance #TestAutomation #AIQuality #PromptEngineering #RAG #SoftwareTesting #AIEthics #TestingInnovation #FutureOfQA #TechSkills #CareerDevelopment #AIModels #QAEngineer #MLOps

𝗙𝗿𝗼𝗺 𝗠𝗮𝗻𝘂𝗮𝗹 𝗤𝗔 𝘁𝗼 𝗔𝗜-𝗔𝘀𝘀𝗶𝘀𝘁𝗲𝗱 𝗤𝗔: 𝗔 𝗥𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 (𝗦𝗸𝗶𝗹𝗹𝘀 + 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀) If you’re currently in Manual QA and want to move toward AI-assisted QA or testing AI-powered applications, you don’t need to learn everything at once. Here’s a realistic roadmap that actually works. 1️⃣ Strengthen Your QA Foundations First Before jumping into AI tools, ensure your testing fundamentals are strong. Focus on: • Test case design techniques • Exploratory testing • API testing • Bug analysis & root cause analysis • Understanding system architecture 💡 Why this matters: AI tools can generate tests, but only a skilled QA engineer can validate whether they are meaningful. 2️⃣ Learn Automation Basics AI-assisted QA heavily relies on automation frameworks. Start with: • Selenium / Playwright • API Automation (Postman / Rest Assured) • CI/CD basics (GitHub Actions, Jenkins) 📌 Mini Project Idea: Build a simple automation suite for a demo web application and integrate it with CI/CD. This teaches you how modern testing pipelines actually work. 3️⃣ Start Using AI in Your Daily QA Workflow You don’t need to build AI models to benefit from AI. Start using tools like: • GitHub Copilot • ChatGPT • AI-based test generation tools • AI debugging assistants Use AI for: • Generating test cases • Writing automation scripts • Creating test data • Debugging failed test cases 💡 The goal is to become an AI-augmented tester, not just a manual tester. 4️⃣ Learn Basics of AI & Machine Learning (For QA) You don’t need to become a data scientist. But understanding these concepts helps a lot: • Machine Learning basics • Model training & datasets • AI bias & hallucination risks • Model evaluation & accuracy Learn concepts like: • Precision • Recall • F1 Score These are key metrics when testing AI systems. 5️⃣ Learn Testing for AI Products Testing AI products is different from traditional software testing. You need to validate: • Model accuracy • Edge cases • Bias in outputs • Data quality • Prompt behavior 6️⃣ Build Small AI-Focused QA Projects Projects are what truly build credibility. Ideas you can build: ✔ AI Test Case Generator ✔ Prompt testing framework ✔ Automated bug classification tool ✔ AI chatbot testing scenarios Even a small GitHub project can show that you understand AI-driven testing workflows. 7️⃣ Become a “Quality Engineer” Instead of Just “Tester” The future QA role looks like this: Manual QA → Automation QA → AI-Assisted Quality Engineer A modern QA engineer should know: • Testing strategy • Automation frameworks • CI/CD pipelines • AI testing concepts • Observability & monitoring Final Thought The biggest mistake testers make is waiting for the “perfect learning path.” The better approach is: Learn → Apply → Build → Share → Repeat. #AITesting #ManualTesting #AutomationTesting #FutureOfQA #QA #SoftwareQuality #LearnWithRushikesh #TestAutomation

I've been in test automation for 20 years. If I were starting my SDET career today — in 2026, with AI changing everything — here are the 5 skills I'd learn first: 1. Playwright over Selenium. Selenium still works. But Playwright is faster, more reliable, and what most modern teams are adopting. Learn the tool companies are hiring for tomorrow, not yesterday. 2. API testing before UI testing. 80% of bugs I've seen in production were API-level issues that UI tests would never catch. Start with Postman, then move to automated API test suites. This is where SDETs make their biggest impact. 3. CI/CD pipeline literacy. If you can't plug your tests into a GitHub Actions or Jenkins pipeline, you're not an SDET — you're a script writer. Understand how your tests run in the real world. 4. AI-assisted test generation. Claude, Copilot, Cursor — learn to use AI to write test scaffolding faster. But more importantly, learn to validate what AI generates. The SDET who can use AI as a tool while catching its mistakes is worth double. 5. Business logic thinking. The hardest skill to teach. Don't just test that the button works. Test that the system behaves correctly when unexpected things happen. This is the skill that separates a £35k QA from a £65k SDET. Notice what's NOT on this list? A computer science degree. Every one of these skills can be learned in 6–9 months with the right structure. Want to know which of these you should prioritise based on your background? I do free 30-minute strategy calls exactly for this. Book yours — link in my profile. #SDET #TestAutomation #Playwright #APITesting #CICD #TechSkills #CareerGrowth #QualityEngineering

AI testing is not about tools, it's about thinking. You can learn tools like DeepEval or Promptfoo in days. But understanding how to truly evaluate AI systems? That takes experience, judgment and strategy. AI does NOT fail like traditional software. It does NOT just break, it misleads, shifts behavior and makes inconsistent decisions. That’s why real AI testing requires: a. evaluating decisions, not just outputs b. testing boundaries, not just accuracy c. analyzing consistency across variations d. understanding business risk, not just technical results Tools help you execute but human intelligence defines what actually matters. This is exactly why I created Human Intelligence Software Testing (HIST) to bring back critical thinking, accountability and real quality into testing, especially in the AI era. If we want testing to be great and respected again, we need to stop relying on tools alone and start thinking like investigators.

🔑 Episode 2 of our AI Testing Guide Series is here! In Episode 1, we explored why AI testing is different from traditional QA. Now, in Episode 2, we dive into the core skills every AI tester needs. From understanding data as the “new requirements” to reading confusion matrices like test case results, this episode is packed with real-world examples and practical mini-demos: * Mislabelled cats & dogs → how bad data breaks models * Spam filters → why precision & recall matter more than “pass/fail” * ML lifecycle basics → training, validation, inference * Tools like TensorFlow, PyTorch, Hugging Face If you’re serious about becoming an AI-ready QA, this is the foundation you need. Stay tuned, Episode 3 will be all about hands-on AI testing techniques. 🚀 #AITesting #QualityPulse #QPA #AIQA #SoftwareTesting #DataDrivenQA #LevelUpQAwithKumudu #FutureOfQA #TrustInAI #QACommunity #KumuduGunarathne #DailyQA #FromSriLankaToWorld #AIWithQA #QualityPulseAcademy

As a Manual Tester, What Should You Know About AI Agents? Let’s clear one thing first. You do NOT need deep AI knowledge or maths to start working with AI agents. Think of an AI agent as a super-fast junior tester. Helpful, efficient, but not always right. Here’s what every manual tester should understand: 1. Prompting matters AI works on instructions. A vague prompt gives vague results. Clear, detailed prompts lead to usable test cases and insights. 2. Tools an AI agent can use AI agents work with tools like browsers, APIs, and test tools. Many testers already pair AI with existing tools, for example: ChatGPT + Playwright for AI-assisted testing ChatGPT + Jira for test case and bug refinement ChatGPT + Postman for API understanding The real value comes from knowing how to review and validate what AI suggests. 3. Workflows are key AI performs best when testing is broken into clear steps: Understanding requirements Identifying scenarios Creating test cases Executing tests Reporting issues This is already how manual testers think. AI simply follows the structure you provide. 4. Validation is your responsibility AI can miss edge cases, assume functionality, or generate incorrect test cases. Always cross-check the output with requirements and real application behavior. 5. Bias and hallucinations are real AI can sound confident and still be wrong. Never treat AI output as the final truth. 6. Basic API knowledge helps You don’t need to code, but you should understand what an API is, request and response flow, common status codes, and basic JSON. This makes AI-assisted API testing far more effective. 7. How AI agents work AI agents do not think like humans. They predict the most likely answer based on patterns, not real understanding. 8. AI agent workflow levels Basic level: Single prompt and single response Intermediate level: Multi-step workflow with tools Advanced level: Autonomous end-to-end testing As a manual tester, focusing on basic and intermediate levels is more than enough to start. Final rule to remember: Always double-check the response you get from the prompt. AI is an assistant, not a replacement. Your testing mindset, judgment, and validation skills are still the real superpower. please let me know if my simplified AI posts are helpful. Your one comment will serve as a motivation. thank you so much! ✌️🙏 Also, read & save my articles on Medium. Follow: muktaqa12 on medium. #SoftwareTesting #QualityEngineering #AITesting #FutureOfQA #TestersOfLinkedIn

Testing with AI: Post 3 ------------------------- Continuing my Testing with AI series, today I’m sharing another amazing AI-powered assistant in software testing—TestCraft. What is TestCraft? In their own words: ============================ TestCraft is a Chrome extension designed to be a companion to your software testing. With TestCraft, you can select UI elements directly from your browser and leverage the capabilities of large language models (LLMs) to generate innovative test ideas, write automation scripts across various frameworks and programming languages, and perform accessibility checks. What makes TestCraft special? ============================ 1) It’s open source—easy to start, and free to explore. 2) You can install it as a Chrome extension, select elements directly on the page, and automate them effortlessly. 3) It supports popular frameworks like Cypress, Selenium, and Playwright. 4) Programming languages supported: JavaScript, TypeScript, Java, C#, and Python. My experience with TestCraft: ====================== I tried using it with Cypress and JavaScript, and here’s what I found: (1) It automatically generated a variety of test cases, including edge cases, which saved me a lot of time. (2) The tool provided a great starting point, though some minor tweaks were needed—particularly around error messages. But honestly, that wasn’t a big deal. (3) For beginners in automation, this tool is fantastic, offering a solid base on which to build. (4) Even experienced testers will find it useful, as it generates a comprehensive list of test cases, saving time on repetitive tasks. The best part? It’s so simple to use! Just install the extension, select elements on the page, and let TestCraft do the rest. While it’s not perfect yet (minor adjustments are required), it’s worth trying out. I’ve attached some screenshots from my experience with the tool to give you an idea. Give it a shot—you might just save yourself hours of work! Link -> https://lnkd.in/gZityxRR #TestingWithAI #TestCraft #AutomationTools #Cypress #Selenium #Playwright #Efficiency #ABAutomationHub