Agentic Workflow

Claude Code

Anthropic's command-line AI coding agent. Unlike chat-based AI tools, Claude Code runs directly in the terminal with read/write access to the project file system. It reads existing test files, runs commands, generates code that matches project conventions, commits changes via git, and connects to external tools through MCP servers. The key distinction from autocomplete assistants like GitHub Copilot is agency: Claude Code accepts high-level multi-step instructions and executes them autonomously, checking in for approval before destructive actions.

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.

Prompt Engineering

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.