Prompt Engineering
// Definition
The craft of writing inputs to AI tools — language models, chat assistants, coding assistants — so that the output is useful, specific, and aligned with the task. Core principles include being specific about format, providing project context (existing patterns, conventions, examples), asking for chain-of-thought reasoning, enumerating edge cases up front, and iterating across multiple turns rather than expecting a perfect first response.
// Related terms
AI Testing
The use of AI — language models, machine-learning classifiers, and AI-powered platforms — to accelerate testing tasks: generating test code from descriptions, analysing logs and stack traces, suggesting edge cases, healing broken locators, comparing screenshots intelligently, and triaging failures. AI augments QA engineers; it does not replace the judgement, exploration, and risk-modelling work that humans still do best.
GitHub Copilot
An AI coding assistant built by GitHub and Microsoft, powered by OpenAI models. It runs as an IDE plugin (VS Code, JetBrains, Visual Studio, Vim) and produces inline code suggestions as you type, plus a chat panel for explanations, fixes, and test generation. Widely adopted by QA engineers for accelerating test authoring; output requires human review for hallucinated APIs and incorrect assertions.
Learn more · AI Tools for QA
Chapter 2 · Lesson 4: Prompt Engineering for Test Automation