Hi, I'm Zoltan a Senior QA Specialist focused on AI evaluation, LLM testing, RAG quality, and reliable automation. This blog is for manual testers, automation engineers, and QA professionals who want to grow into the future of AI testing.
Manual testing, automation, and exploratory thinking still matter. AI testing builds on those skills and adds evaluation, safety checks, data quality, and production monitoring.
Exploratory testing, user empathy, risk thinking, and clear bug reports are still the foundation.
Automation turns repeatable checks into pipelines, quality gates, and release confidence.
AI testing adds rubrics, datasets, LLM judges, prompt security, RAG checks, and drift monitoring.
AI outputs vary. Reliable testing depends on rubrics, curated datasets, statistical signals, and calibrated LLM-as-judge patterns.
Prompt injection, jailbreaks, data leakage, and unsafe tool use make AI security testing a core quality responsibility.
Model, prompt, and retrieval changes can shift behavior silently. Production monitoring and regression evaluation are essential.
Generative AI can return a perfect 200 OK response while still producing hallucinated, biased, or unsafe output. This is why AI teams need structured check not just vibe checks.
The AI in QA conversation is loud, fear driven, and often unhelpful. QA Evolve exists to replace hype with practitioner level guidance on testing AI systems and growing QA careers responsibly.
I'm Zoltan, a Senior QA Specialist based in Budapest with over 12 years of experience in software quality. My work covers manual testing, automation architecture, CI/CD quality practices, QA leadership, and security-focused testing. Today, I focus on helping QA professionals understand AI evaluation, LLM testing strategies, RAG system reliability, and practical guardrails for real-world AI applications.