Claude Code

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.

Model Context Protocol (MCP)

An open standard introduced by Anthropic in late 2024 that lets AI assistants connect to external tools and data sources through a uniform JSON-RPC interface. An MCP server exposes tools (callable functions), resources (readable data), and prompts (templates) to any MCP-compatible host (Claude Desktop, Claude Code, IDE plugins). Build a server once and any compliant client can use it — the protocol is model-agnostic, which makes integrations portable across AI providers.

Agentic Workflow

A multi-step AI task where the model plans, executes, and self-corrects autonomously rather than responding to a single prompt. In an agentic workflow, the AI reads files, runs commands, processes results, makes decisions, and loops until the goal is complete — checking in at defined checkpoints for human approval. Claude Code's /plan mode and sub-agent capabilities are examples. Effective agentic workflows require well-scoped goals, explicit checkpoints, and incremental commits so errors can be caught and reversed.