Learning Paths for Technical Professionals
AI for QA/Software Testers
This starter learning path empowers QA and software testers to leverage AI for enhanced productivity and robust model testing. Covering generative AI, AI agents, automation tools like Selenium, Playwright, Cypress, GitHub Copilot, K6, and ChatGPT, it also delves into AI model evaluation, RAG-LLM testing, responsible AI, and integration of AI into manual and automated testing workflows.
Learning objectives:
- Apply Generative AI and AI Agents: Utilize AI to automate test planning, case generation, test data creation, and code development for various automation frameworks.
- Integrate AI Tools in Test Automation: Seamlessly incorporate tools like GitHub Copilot, ChatGPT, and low-code AI-powered platforms into existing manual and automated testing processes.
- Conduct AI Model Testing and Evaluation: Master techniques for testing machine learning models, including supervised, unsupervised, and reinforcement learning, and evaluate LLMs using frameworks like RAGAS and Langchain.
- Implement Responsible and Secure AI Testing: Ensure fairness, transparency, privacy, and security in AI model testing and deployment, adhering to industry best practices.
- Optimize API and Performance Testing with AI: Enhance API and performance testing using ChatGPT with K6, and AI-driven prompt engineering to streamline workflows and improve test coverage.
Target audience:
This path is designed for QA professionals, software testers, and automation engineers seeking to enhance their productivity and skill set with AI-driven tools and methodologies. It is also suitable for those interested in testing AI/ML models and integrating AI into manual and automated testing frameworks. Prior experience in software testing or automation is beneficial but not required.