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

***Practical Tip #1 for AI Testers*** --> TEST DECISIONS, NOT FEATURES Traditional software testing focuses on features: Does the button work? or Does the API return the expected response? AI systems are different. They do NOT just execute logic, they make decisions based on probabilities, patterns and learned behavior. As a QA Tester testing AI functionalities or platforms, your job is to evaluate the quality of those decisions, NOT just whether the system responds. What this means in practice: 1. Test the outcome, not the output 2. Ask whether the decision makes sense in context, even if the response is technically correct or well-formatted 3. Evaluating whether a decision or outcome makes sense in context, NOT just whether it is technically correct 4. Would a knowledgeable human make the same decision given the same information? If not, why? 5. Check consistency across similar scenarios 6. Slightly vary inputs and observe whether decisions remain stable. Large swings often signal weak decision logic 7. Assess whether a decision, outcome, or action can be justified, explained and defended to others if it is questioned. 8. If a stakeholder, auditor, or regulator asked “Why did the AI do this?”, could the answer be clearly explained? 9. Ensures confidence matches reality and identify overconfidence 10. Watch for decisions delivered with high confidence when uncertainty should exist. Confident wrong decisions are high risk 11. Look beyond happy paths 12. Test ambiguous, incomplete, or conflicting inputs because real users rarely provide perfect data 13. Tie decisions to business impact. A technically acceptable decision may still be harmful if it leads to user frustration, financial loss, or legal exposure This shift from testing features to testing judgment is one of the most important mindset changes QA/Testing Professionals must make when working with AI. Follow me for more FREE tips, FREE AI webinars and seminars for testers, FREE E-learning courses, QA Mentor AI Testing job opportunities and much more.... Join the Human-Governed AI Testing Community https://lnkd.in/e3ybXQvP to gain practical AI testing insights, real-world lessons, shared mistakes and successes, and a human-centered mindset that helps you grow as an AI testing professional.

🚀 Step by step roadmap how I switched into AI & LLM testing: 1. Career break. Taking a career break, not easy and not for everyone, for me it was important - it gave me space to reset and reflect. Since I want to share real story, I included this step too :) 2. Researching the market. I tried to understand what is possible for me, what I actually want to do, what fits my 12+ years QA background. 3. Learning AI. I started reading and watching everything AI. At first, I thought it’s just about using AI tools for QA. Then it clicked - I want to actually test AI itself - LLMs, AI-driven apps and services.  4. Finding suitable mentor, program, career acceleration course. I found that learning in a group, following routine and building discipline worked best for me. DM me if you want to know the course I took, I can share it with you.  5. Learning the risks unique to AI. Unlike traditional apps, AI models can: - Hallucinate (make up facts) - Be prompt-injected (tricked into revealing or doing unintended things) - Show bias or unfair responses - Behave inconsistently across the same inputs 6. Exploring emerging frameworks. I started experimenting with tools like Promptfoo, LangTest, LM Studio, Hugging Face. 7. Learning to red-team AI systems. This skill is essential. Companies need testers who must be able to simulate prompt injections, jailbreaks, and ethical drifts to keep systems safe. 8. Building my portfolio. Creating GitHub repo, saving all my projects there so I can share. Adding case studies → short README files explaining what I tested, why it matters, and my findings. Documenting experiments. Contributing to open source. ❓What should I go deep in to in my next post? Building AI QA portfolio or Promptfoo framework? ❓What else would you add to the list? #AITesting #Promptfoo #LLMtesting #QualityAssurance #TestAutomation #CareerGrowth