Introduction to AI in Software Testing - Chapter 1 (Path to AI QA Engineering) 🤖
AI Summary
Summary of Video: Path to AI QA Engineer
Introduction
- Overview of the journey into AI QA engineering, shared by an experienced test engineer.
Transition to AI QA Engineering
- The speaker highlights their two-year learning period covering AI tools and techniques.
- Importance of understanding AI beyond traditional QA roles.
Learning Path Options
- Various paths to become an AI QA Engineer:
- Educate yourself from the ground up in AI and large language models.
- Focus on applications of AI rather than just model development.
Role of Current QA Engineers
- QA Engineers now need to handle manual testing, automation, and various programming languages.
- Evolving role of AI in QA, emphasizing the need for AI-powered automation solutions.
Job Expectations
- Companies look for advanced skills in developing AI-driven QA solutions.
- Expectations include functional testing of AI models and adapting to emerging AI technologies.
Becoming an AI QA Engineer
- Learn generative AI tools and APIs (e.g., ChatGPT, Gemini API).
- Basic knowledge of transformer models and the significance of the paper “Attention Is All You Need”.
- Understand AI agents and their capabilities.
Evaluation Techniques
- Evaluation of large language models through various metrics and using LLMs as judges for testing.
- Techniques such as static evaluation metrics and answer relevancy to assess performance, biases, robustness, and scalability.
Learning Resources
- Suggestions of courses and materials for further learning in AI QA engineering.