Gen AI for QA: Playwright, Copilot & Claude Code

Explore how AI-powered testing assists QA engineers in daily work, review available QA tools, and understand core AI concepts and limitations for a balanced, practical overview.

Discover how AI-powered tools transform QA workflows by generating test cases and edge cases, assisting with code, creating test data, brainstorming ideas, and extracting information from specifications.

Explore ai tools for qa automation, including LLMs-powered chatbots and MCPs capabilities, such as ChatGPT, Claude, Google Gemini, code assistants like GitHub Copilot, plus Notebook LM and N8N.

Explore how generative AI and large language models power testers, from prompts and tokens to AI agents that read and write files, run tests, and reason with tool use.

Explore common AI powered testing limitations, including high token costs, non-determinism, hallucinations, and privacy concerns, and learn practical strategies like prompt optimization and using AI to augment, not replace, experience.

Gain practical skills in prompt engineering for QA by designing, refining, and testing prompts to improve accuracy and relevance, using Claude or other chat tools.

Master prompt engineering for software testing by crafting clear, specific prompts with constraints and context to generate structured login tests (email, password, sign-in) with positive, negative cases and a format.

Discover how prompt constraints shape reliable AI testing by assigning roles, defining scope, quality, format, and exclusions, with a practical QA analyst example.

Master ai powered test automation emphasizes context engineering to guide prompts with page content, code snippets, and logs, enabling precise, model-driven qa tasks.

Explore prompt patterns for qa engineers, including zero-shot, few-shot, chain-of-thought, and iterative conversation prompts, with examples guiding LLM outputs for bug reports and testing tasks.

Identify common prompt engineering mistakes in software testing and learn fixes, including avoiding vague prompts, splitting tasks, applying clear constraints and role assignments, and providing context.

Learn QA skills with an AI client like Cloud Code using prompts to build quizzes on functional testing and Selenium. Generate up-to-date quizzes directly from official framework documentation.

Explore a quick prompt engineering tip for faster test automation by recording a prompt to generate realistic test data, including valid and invalid cases for a registration form.

Move into hands-on AI assisted coding for test automation, using Playwright with GitHub Copilot or Claude code, supported by resources and case studies.

Explore how GitHub Copilot enhances test automation with Playwright in VS Code, enabling AI-assisted coding and setup with Node.js and npm to run tests on the Awesome Pizza app.

Install and configure GitHub Copilot in Visual Studio Code, sign in with GitHub, and use Agent or Ask modes with model selection and Web Fetch tools for website testing.

Install and enable the GitHub Copilot plugin in IntelliJ IDEA, sign in with GitHub using the device code, and access GitHub Copilot suggestions and chat to accelerate code generation.

Initialize a Playwright test project with GitHub Copilot in VS Code, using TypeScript and test folder. Install browsers, remove sample code, and start writing tests for awesome pizza with Copilot.

Generate an end-to-end testing strategy for a fresh web app using GitHub Copilot’s fetch tool, create Playwright tests (menu, cart, order), and save the plan as a markdown reference.

Review and fix tests with GitHub Copilot, resolving failing cases and unicode issues, and replace hard-coded menu data with dynamic checks against backend data.

Advance end-to-end testing with GitHub Copilot for Playwright by generating and validating tests for the order placement spec.ts, using fetch commands and curl in VS Code.

Explore the Playwright MCP for QA automation, enabling natural-language browser actions and auto-generated tests. See how GitHub Copilot and VS Code MCP extend testing workflows.

Experience how GitHub Copilot in VS Code offers AI-powered coding assistance with shadow text and inline suggestions, accepts via tab, and supports chat, explain, and code review for testing.

Explore how the page object model in Playwright enables maintainable tests, with AI-assisted generation via GitHub Copilot to create menu.page.ts and integrate into the code base.

Explore Cloud Code as an AI coding assistant, an extension to the Cloud AI client, to write and debug tests for a web store app.

Install and configure Claude code using a terminal or VS Code extension, noting the paid plan requirement, and complete setup by selecting a theme and authenticating with a Claude account.

Explore the basic functions of Cloud Code in your workspace, using terminal or the VS Code extension with slash commands to explain code, such as menu.spec.ts.

Learn to initialize a Playwright project using cloud code in VS Code, generate and debug tests for a sample pizza site, and manage changes with Git.

Generate a testing strategy for the address app using Cloud Code in the VS Code extension, analyze the Playwright project, then write and save a comprehensive file-based test plan.

Use Claude-powered Cloud Code to generate tests from a plan, implement a page object model, and replace hard-coded menu items with a getMenuItems method to ensure tests pass.

Learn how Claude memory works, focusing on the Claude.md file for persistent context across projects, with .init to generate and customize coding guidelines and TS conventions.

Complete the testing suite for the dump application by using cloud code to generate seven specification files and run tests with Playwright. Fix assertion issues and ensure all tests pass.

Switch Claude Codd models (Sonnet, Opus, Haiku) to balance capability and token use for testing in general, then use insights command to generate a usage report.

explore ai-assisted api testing, generating api collections, transforming data formats, and documenting apps with swagger using an ai client on the awesome pizza application.

Generate swagger api documentation from a back-end project with an ai client, producing swagger.json and in-app swagger ui to reveal endpoints like menu, orders, and authenticated calls for QA.

Generate and debug an API requests collection for QA automation using AI assistants, transforming data formats, importing into Postman or Bruno, and validating requests in VS Code.

Explore agentic AI and MCP servers, and extend their capabilities in chat and AI applications with MCPs. Analyze Chrome DevTools MCP and Playwright MCP designed for QA work.

Compare agentic AI with non-agentic workflows, and see how agentic AI autonomously fetches information, fixes tests, and uses QA tools like GitHub Copilot via the Model Context Protocol.

Learn how the model context protocol, or MCP, standardizes AI tools for external systems. See a pizza web store MCP example to enable AI agents like Copilot to order pizza.

Find MCP servers through the Awesome MCP Servers collection on GitHub, browse browser automation options like Chrome DevTools and Playwright, and review safety and popularity indicators.

Learn to install and run MCP servers using Node.js or Python, with npx for TypeScript servers and uvx or pip for Python servers, plus package managers and virtual environments.

Install a basic MCP server in Claude via Cloud Desktop, add the Airbnb MCP entry to a valid JSON, restart Cloud, and verify by retrieving apartments and a map.

Install an MCP server in VS Code with GitHub Copilot by editing .vscode/mcp.json and selecting the Airbnb entry; start the server to access Airbnb listing and search.

Learn to interact with a live Chrome browser using natural language through the Chrome DevTools MCP, gaining performance audits, debugging, and network insight, with Puppeteer under the hood.

Explore the Playwright MCP for browser automation via natural language, and learn to generate Playwright tests from descriptions, with VS Code setup and assertions.

Explore AWS documentation MCP servers for QA engineers. Configure mcp.json, install MCP servers with uv and uvx, and use read and search documentation tools in VS Code.

Define AI skills as reusable instructions for AI clients—first introduced by Claude and used by Copilot and ChatGPT—loaded into context to speed up tasks and reduce repetition, stored as skill.md.

Build your first ai skill by creating a hello world skill with a skill.md, a name and description, and a template, then extend with scripts like awesomename.js.

Analyze two QA skills: a test-from-ticket skill that generates a test plan with test cases from acceptance criteria using a template, and a count tests script that scans project files.

Identify a workflow to automate with clear input and output data, then implement using skill.md, templates, and scripts. Iterate with an AI assistant to refine the skill for day-to-day tasks.

Discover ready-made ai skills for test automation and learn to install them via mpx, then integrate them into your project using resources like ClaudeSkills and skills.sh.

Orchestrate Playwright AI agents to generate, run, and fix tests for a Hello World application, using a test plan, browser interactions, and a healer agent.

Compare token usage between Playwright MCP and Playwright CLI on the same pizza store task, using cloud code and haiku model, highlighting token and context efficiency.